Slide 34: ML/AI: A Moment in Time (2025)
ML/AI: A MOMENT IN TIME (2025)
This slide is the governance and workforce view of AI in 2025: what to experiment with, what to standardize, and what to sunset.
LIFECYCLE POSITIONING:
| Stage | Technologies |
|---|---|
| Bleeding Edge | Artificial General Intelligence (AGI), Neuromorphic Computing (Intel Loihi 2, IBM NorthPole), Quantum Machine Learning |
| Leading Edge | AI Agents (autonomous multi-step), Multimodal Foundation Models (GPT-4o, Gemini, Claude), On-device/Edge LLMs (Apple Intelligence, Gemini Nano), AI Code Generation (Copilot, Claude Code, Cursor) |
| Mainstream | Large Language Models (ChatGPT, Claude, Gemini), Image Generation (Midjourney, DALL-E, Stable Diffusion), MLOps Platforms (MLflow, W&B, SageMaker), Recommendation Systems (Netflix, Spotify, Amazon) |
| Trending Behind | Traditional ML (sklearn pipelines, XGBoost), Rule-based Expert Systems, RNNs/LSTMs for NLP |
| End of Support | First-gen Chatbots (keyword-based), TensorFlow 1.x |
| End of Life | Expert System Shells (CLIPS, Jess), Symbolic AI Frameworks (Cyc, Prolog-based) |
KEY INSIGHTS:
- The mainstream is only ~5 years old - LLMs went from research curiosity to enterprise standard in record time. ChatGPT (Nov 2022) accelerated enterprise adoption by 5-10 years
- Leading edge is moving at unprecedented speed - AI agents, multimodal models, and code generation tools are evolving monthly, not annually. The leading-to-mainstream transition may be the fastest in technology history
- Traditional ML is already "trending behind" - sklearn pipelines and XGBoost dominated 2015-2022 but are being displaced by foundation models for many tasks. This transition happened in under 5 years
- The AI winter artifacts are visible at the bottom - expert system shells (1980s) and symbolic AI frameworks represent the previous AI paradigm. Their position in End of Life shows how completely the field has pivoted
- AGI remains firmly bleeding edge - despite media hype, there is no scientific consensus on timeline, definition, or even feasibility. It's the "DNA storage" of the AI world - transformative if achieved, but years (or decades) away
DECISION LENS (GOVERNANCE + TALENT):
- Define tiered controls by lifecycle stage (experiment, limited production, enterprise standard).
- Align workforce plans to fast-moving stage changes (reskill from legacy ML to foundation-model workflows).
- Separate hype tracking from adoption policy so AGI narratives do not distort current delivery priorities.
Speaker notes
- "This is the most dynamic moment-in-time snapshot we've seen. The AI landscape is changing faster than storage, web, or supply chain - sometimes quarterly."
- "Look at the bottom: CLIPS and Jess were the 'AI' of the 1980s. Expert systems were supposed to revolutionize business. They're now end-of-life. Will today's LLMs follow the same pattern in 20 years? The lifecycle model says eventually, yes."
- "Traditional ML is trending behind, and that happened shockingly fast. Data scientists who built careers on sklearn and XGBoost in 2018 are now pivoting to LLMs and agents. This is the personal impact of lifecycle transitions."
- "AI agents are the one to watch. They're in leading edge right now - proven concepts, early adoption. If they cross to mainstream, they'll change how we build software. That transition could happen in 2025-2026."
- "AGI is our reality check. Despite the hype, it's firmly bleeding edge - no production use, no clear timeline. Responsible technology adoption means knowing which stage you're actually in, not which stage the marketing says."
Sources:
- Stanford University HAI, "Artificial Intelligence Index Report" (2024) - aiindex.stanford.edu
- Gartner, "Hype Cycle for Artificial Intelligence" (2024)
- State of AI Report (2024) - stateof.ai
- McKinsey, "The State of AI in Early 2024" - mckinsey.com
- NVIDIA, "CUDA Toolkit and GPU Computing History" (2024)