From: aidotengineer
The field of Artificial Intelligence (AI) has seen rapid transformation, with significant advancements in model capabilities alongside persistent challenges and evolving limitations [00:00:06]. Understanding the evolution of AI models involves examining both their progress and the inherent constraints that continue to shape their development.
Early AI Models and Initial Limitations
In 2023, many AI applications were characterized as “AI wrappers,” leading to arguments about their lack of a defensible strategy [00:00:44]. Despite these initial perceptions, the rapid adoption of AI, particularly in areas like coding, demonstrated a clear path for disruption by these models [00:01:14].
However, early AI models faced significant limitations of current AI models in software engineering regarding their performance:
- Hallucinations remain a concern [00:01:41].
- Overfitting continues to be a problem [00:01:43].
- Developers required more structured outputs from models [00:01:45].
For years, making models larger and feeding them more data consistently improved their intelligence [00:02:01]. However, this approach eventually “hit a wall,” with improvements slowing down as models reached their limits on existing tasks, regardless of additional data [00:02:08]. Big jumps in performance, similar to the leap between GPT-3.5 and GPT-4, began to slow down [00:01:57].
Advancements in AI Model Technology
Recent advancements in AI model technology have pushed the field forward through new training methods:
- Real Reinforcement Learning: Models like DeepSeek R1 have been trained without labeled data, learning autonomously [00:02:42]. OpenAI reportedly uses this method for their reasoning models like 01 and 03 [00:02:57].
- Chain of Thought: Reasoning models now employ Chain of Thought thinking at inference time, allowing them to “think” before generating answers, which helps solve complex reasoning problems [00:03:03].
- Expanded Capabilities: Model providers are enhancing models with capabilities such as advanced tool use, research functionalities, and near-perfect OCR accuracy (e.g., Gemini 2.0 Flash) [00:03:24].
Challenges in Testing and Evaluation
Despite these advancements, traditional benchmarks for testing and evaluation of AI models have become saturated [00:03:41]. New benchmarks are being introduced to capture the performance of reasoning models, such as the Humanities last exam, which measures performance on truly difficult tasks [00:03:50]. Even the latest smart models still struggle with these complex challenges [00:04:01].
Beyond Model Performance: Orchestration and Techniques
Success in AI products now relies less on the models alone and more on how systems are built around them [00:04:15]. This has led to the evolution of AI engineering and tools and new techniques:
- Advanced Prompting: Techniques like Chain of Thought for better model interactions [00:04:25].
- Retrieval Augmented Generation (RAG): Important for grounding model responses with proprietary data [00:04:31].
- Memory: Crucial for multi-threaded conversations to maintain context [00:04:42].
- Long Context Windows: Enabled new use cases [00:04:47].
- Graph RAG: For hierarchical responses [00:04:56].
- Agentic RAG: To make workflows more powerful and autonomous [00:05:12].
The process of building reliable AI agents heavily emphasizes a test-driven development approach to identify the optimal combination of techniques, models, and logic for specific use cases [00:05:27].
Current Limitations of Advanced Agentic AI
While AI is moving towards more autonomous agents, a “fully creative workflow” (L4) where AI acts as an “inventor” and creates its own new workflows or utilities is currently “out of reach” [00:20:08]. This is due to existing model constraints:
- Overfitting: Models tend to stick closely to their training data [00:20:14].
- Inductive Bias: Models make assumptions based on their training data, which can limit their ability to innovate in novel ways [00:20:20].
The goal remains AI that can invent, improve, and solve problems in ways not previously conceived [00:20:30]. While there is significant innovation happening in less advanced agentic levels (L1 and L2), L3 and L4 are still constrained by current model capabilities and surrounding logic [00:22:22].