Publishing AI Research on Older Models Isn’t Inherently Bad, But It Hides a Trap

The academic rhythm clashes with AI pacing

Wharton School professor Ethan Mollick has addressed a growing issue in academic AI research. Many published papers examining the impacts of AI evaluate older models (like GPT-3.5) simply because the gap between data collection and peer review spans many months. According to Mollick, this is not inherently bad when proving a concept. If research shows that an older generation of AI can perform a task, it remains a valid finding.

However, he points out the significant limitations of this approach. Academics must be extremely careful when drawing conclusions. Once an older AI model demonstrates a specific capability, it is highly likely that newer versions will be even more efficient and cost-effective at that same task.

Inaccurate data creates a false sense of security

While the technical community understands that models improve exponentially, this might not be obvious to non-technical readers or corporate managers. The problem arises when a research paper demonstrates an AI’s “inability” to solve a specific problem based on an old model, without clearly emphasizing that a newer version has likely already overcome that limitation.

This delay can lead to dangerous managerial decisions. If a company decides to delay AI adoption based on an academic study proving AI unreliability, it may be building its strategy on data that hasn’t reflected reality for a year. Therefore, careful discussion of results is a necessity, not just a formal exercise in methodology.

Where old data stops being useful

This mismatch between the pace of development and the speed of publishing means that the traditional peer-review process is beginning to fail in applied AI research. Research on older models isn’t useless for understanding core principles, but it is completely unsuitable for predicting technological limits.

This creates a double standard: while capabilities demonstrated by older AI models can be considered a reliable baseline for future development, the failures of old models tell us absolutely nothing about future capabilities.

Valuing methodology over specific results

The proof of how academia will handle this problem won’t be a faster publishing process, but a shift in evaluation methodology. The most valuable studies will be those that can test abstract concepts independently of the specific LLM running in the background.

Lilith's verdict

Proving the capabilities of older models is valuable. Proving their limitations and telling a lay audience that "AI can't do this" borders on spreading misinformation under the guise of science.