A developer's experiment with GPT-5-Codex for AI research has sparked debate about the current capabilities and limitations of AI-assisted research. The project involved training small language models within a five-minute timeframe, revealing both the potential and boundaries of current AI tools.
Cost Structure:
- GPT-5-Codex usage: $200 USD per month for intensive research
- Token consumption: Restart required every million tokens
- Training time constraint: 5-minute maximum for model training
The Reality Check: Amateur vs Professional Research
The community response highlighted a crucial distinction that the original experiment overlooked. Several experienced practitioners pointed out that the comparison wasn't particularly meaningful, as it involved someone without formal AI research background competing against an AI system on a relatively simple task. The work described was characterized as being at the level of an undergraduate student's first natural language processing course rather than genuine research.
This observation raises important questions about how we evaluate AI capabilities. When AI tools outperform amateurs in specialized fields, it doesn't necessarily indicate that professional-level work is at risk. Instead, it suggests that AI is currently most effective at lifting the floor - helping beginners achieve basic competency more quickly.
Technical Results Comparison:
- Original manual approach: 1.8M parameter transformer, 9+ perplexity
- AI-assisted best result: 8.53 perplexity (3 layers, 4 heads, 1441 dimension)
- N-gram distillation method: Best qualitative output with coherent story structure
- Shallow fusion approach: 7.38 perplexity but poor text quality
The Economic Implications of AI-Assisted Work
A significant concern emerged around the economic impact of these tools. Community members expressed worry about skill devaluation and job displacement, particularly for mid-career professionals. The discussion revealed anxiety about a scenario where management might reduce team sizes based on perceived AI productivity gains, while the remaining workers face increased workloads of reviewing AI-generated code rather than engaging in creative problem-solving.
Some folks see it as a way to remove hard problems so they can focus on drudgery. Do you love to code, but hate code reviews? Guess what you get to do more of now!
The $200 USD monthly cost for intensive AI usage also highlighted accessibility concerns, suggesting that effective AI-assisted research might become a privilege available mainly to well-funded individuals or organizations.
Technical Limitations and Trust Issues
The experiment revealed several technical limitations that experienced users have encountered across different AI research tools. Many reported that AI systems perform well initially but eventually hit a wall requiring human intervention and debugging. This pattern suggests that current AI tools are most effective for setup and initial exploration rather than sustained, complex problem-solving.
Trust emerged as another critical issue, particularly with AI research systems that have limited access to certain data sources or show inconsistent reasoning capabilities. Users noted problems with AI systems incorporating unreliable sources or failing to distinguish between legitimate scientific information and pseudoscience, especially in fields like health and medicine.
AI Research Workflow:
- Codex modifies training script and runs 3-4 experiments (~20 minutes)
- AI suggests 2-3 next approaches based on results
- Human selects approach or occasionally suggests alternative
- Process repeats with periodic GPT-5-Pro consultation
The Future of AI-Assisted Research
Despite the limitations, some practitioners reported positive experiences with vibe coding and AI-assisted research workflows. The key appears to be understanding these tools as productivity enhancers for specific tasks rather than replacements for deep expertise. The most successful applications seem to involve using AI for rapid prototyping, parameter sweeping, and handling routine coding tasks while humans focus on higher-level strategy and validation.
The debate ultimately reflects broader questions about AI development and deployment. While current tools show promise for accelerating certain aspects of research and development, they remain far from the autonomous research capabilities that some marketing materials might suggest.