MLA 030 AI Job Displacement & ML Careers
Machine Learning Guide
ML engineering demand remains high with a 3.2 to 1 job-to-candidate ratio, but entry-level hiring is collapsing as AI automates routine programming and data tasks. Career longevity requires shifting from model training to production operations, deep domain expertise, and mastering AI-augmented workflows before standard implementation becomes a commodity. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want Market Data and Displacement ML engineering demand rose 89% in early 2025....
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MLA 029 OpenClaw
Machine Learning Guide
OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want OpenClaw is a self-hosted AI agent daemon (Node.js, port 18789) that executes autonomous tasks via messaging apps like WhatsApp or Telegram. Developed by Peter...
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MLA 028 AI Agents
Machine Learning Guide
AI agents differ from chatbots by pursuing autonomous goals through the ReACT loop rather than responding to turn-based prompts. While coding agents are currently the most reliable due to verifiable feedback loops, the market is expanding into desktop and browser automation via tools like Claude co-work and open claw. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want Fundamental Definitions Agent vs. Chatbot: Chatbots are turn-based and human-driven. Agents receive...
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MLA 027 AI Video End-to-End Workflow
Machine Learning Guide
How to maintain character consistency, style consistency, etc in an AI video. Prosumers can use Google Veo 3’s "High-Quality Chaining" for fast social media content. Indie filmmakers can achieve narrative consistency by combining Midjourney V7 for style, Kling for lip-synced dialogue, and Runway Gen-4 for camera control, while professional studios gain full control with a layered ComfyUI pipeline to output multi-layer EXR files for standard VFX compositing. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI...
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MLA 026 AI Video Generation: Veo 3 vs Sora, Kling, Runway, Stable Video Diffusion
Machine Learning Guide
Google Veo leads the generative video market with superior 4K photorealism and integrated audio, an advantage derived from its YouTube training data. OpenAI Sora is the top tool for narrative storytelling, while Kuaishou Kling excels at animating static images with realistic, high-speed motion. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want S-Tier: Google Veo The market leader due to superior visual quality, physics simulation, 4K resolution, and , which removes...
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MLA 025 AI Image Generation: Midjourney vs Stable Diffusion, GPT-4o, Imagen & Firefly
Machine Learning Guide
The AI image market has split: Midjourney creates the highest quality artistic images but fails at text and precision. For business use, OpenAI's GPT-4o offers the best conversational control, while Adobe Firefly provides the strongest commercial safety from its exclusively licensed training data. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want The 2025 generative AI image market is defined by a split between two types of tools. "Artists" like Midjourney excel at creating...
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MLG 036 Autoencoders
Machine Learning Guide
Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at - stay healthy & sharp while you learn & code Build the future of multi-agent software with . Thanks to from for recording...
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MLG 035 Large Language Models 2
Machine Learning Guide
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as...
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MLG 034 Large Language Models 1
Machine Learning Guide
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex...
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MLA 024 Agentic Software Engineering
Machine Learning Guide
Agentic engineering shifts the developer role from manual coding to orchestrating AI agents that automate the full software lifecycle from ticket to deployment. Using Claude Code with MCP servers and git worktrees allows a single person to manage the output and quality of an entire engineering organization. Links Notes and resources at - stay healthy & sharp while you learn & code - use my voice to listen to any AI generated content you want The Shift: Agentic Engineering Andrej Karpathy transitioned from "vibe coding" in February 2025 to "agentic engineering" in...
info_outlineML engineering demand remains high with a 3.2 to 1 job-to-candidate ratio, but entry-level hiring is collapsing as AI automates routine programming and data tasks. Career longevity requires shifting from model training to production operations, deep domain expertise, and mastering AI-augmented workflows before standard implementation becomes a commodity.
Links
- Notes and resources at ocdevel.com/mlg/mla-30
- Try a walking desk - stay healthy & sharp while you learn & code
- Generate a podcast - use my voice to listen to any AI generated content you want
Market Data and Displacement
ML engineering demand rose 89% in early 2025. Median salary is $187,500, with senior roles reaching $550,000. There are 3.2 open jobs for every qualified candidate. AI-exposed roles for workers aged 22 to 25 declined 13 to 16%, while workers over 30 saw 6 to 12% growth. Professional service job openings dropped 20% year-over-year by January 2025. Microsoft cut 15,000 roles, targeting software engineers, and 30% of its code is now AI-generated. Salesforce reduced support headcount from 9,000 to 5,000 after AI handled 30 to 50% of its workload.
Sector Comparisons
- Creative: Chinese illustrator jobs fell 70% in one year. AI increased output from 1 to 40 scenes per day, crashing commission rates by 90%.
- Trades: US construction lacks 1.7 million workers. Licensing takes 5 years, and the career fatality risk is 1 in 200. High suicide rates (56 per 100,000) and emerging robotics like the $5,900 Unitree R1 indicate a 10 to 15 year window before automation.
- Orchestration: Prompt engineering roles paying $375,000 became nearly obsolete in 24 months. Claude Code solves 72% of GitHub issues in under eight minutes.
Technical Specialization Priorities
- Model Ops: Move from training to deployment using vLLM or TensorRT. Set up drift detection and monitoring via MLflow or Weights & Biases.
- Evaluation: Use DeepEval or RAGAS to test for hallucinations, PII leaks, and adversarial robustness.
- Agentic Workflows: Build multi-step systems with LangGraph or CrewAI. Include human-in-the-loop checkpoints and observability.
- Optimization: Focus on quantization and distillation for on-device, air-gapped deployment.
- Domain Expertise: 57.7% of ML postings prefer specialists in healthcare, finance, or climate over generalists.
Industry Perspectives
- Accelerationists (Amodei, Altman): Predict major disruption within 1 to 5 years.
- Skeptics (LeCun, Marcus): Argue LLMs lack causal reasoning, extending the adoption timeline to 10 to 15 years.
- Pragmatists (Andrew Ng): Argue that as code gets cheap, the bottleneck shifts from implementation to specification.