AI news story
AI is code – and can't be prompted into being smarter
A recent article argues that current large language models (LLMs) are fundamentally limited by their underlying code and train…
Editor's take
A recent article argues that current large language models (LLMs) are fundamentally limited by their underlying code and training data, suggesting that prompt engineering alone cannot unlock significantly greater intelligence. This perspective challenges the prevailing notion that sophisticated prompting can overcome inherent architectural or data constraints, implying that genuine leaps in AI capability will require architectural innovation and more comprehensive data curation rather than solely refining user input methods.
This distinction matters because it shifts the focus from user-facing techniques to the core development of AI systems. If prompt engineering has reached its ceiling for models like GPT-4 or Claude 3, then the onus is on researchers and developers at companies like OpenAI and Anthropic to invent new model architectures or dramatically expand training datasets to achieve the next level of AI performance, potentially slowing down the perceived rapid progress in AI.
Future developments to monitor include whether new model architectures, such as those exploring modularity or different forms of reasoning, can demonstrably surpass current LLMs. Additionally, observing the economic investment in fundamental AI research versus prompt engineering tools will offer insight into industry priorities. A significant shift in research investment towards foundational model development would validate this perspective.