I gave two AI coding agents the same forecasting challenge. Only one could overcome it.
A nagging forecasting problem had been sitting in my backlog for months. Not complex enough to prioritize, but interesting enough that I knew exactly how I'd approach it. Then Claude Code and Cursor 2.0 landed on my desk, and I realized: perfect controlled experiment.
Could these AI agents execute a full data science workflow with minimal intervention? Would they catch the validation trap I deliberately left out of my specifications?
Spoiler: My job is safe for now. But I'm genuinely impressed.
The Setup
I built identical conditions: same spec-driven design document, same system prompt establishing project requirements, no intervention unless absolutely necessary.
The Trap: I deliberately omitted one critical step: checking for multicollinearity in feature-engineered variables.
In time series forecasting, lagged features and rolling statistics often create highly correlated predictors. If you don't check for this (using VIF scores or correlation matrices), you build models that look good but are actually unstable. The variance in your coefficient estimates gets inflated, making the model fragile and uninterpretable. Any experienced data scientist knows to check for this. Would the AI agents?
Round 1: The First 30 Minutes
Claude Code ran from the terminal with minimal visibility. Productive but opaque.
Cursor 2.0 operated in its IDE, showing clear logical progression. The transparency was reassuring.
After 30 minutes, both hit the same speedbump: package installation approvals. I used this pause to ask each agent to summarize progress.
Both fell into my trap. Neither mentioned multicollinearity checks.
The Intervention
I asked point-blank: "Did you check for multicollinearity?"
Claude Code has specialized agents. I spun up a "PhD Statistician" to review statistical rigor. This agent identified correlated features and established validation checks going forward. The handoff was seamless.
Cursor 2.0 struggled to repivot. It understood what I was asking but had difficulty reweaving validation into its existing workflow.
This is where the tools truly differentiated.
Results
Claude Code (~1 hour):
- Complete forecasting system with proper feature validation
- Comprehensive documentation
- Beat industry-standard MAPE benchmarks
- The multicollinearity check identified redundant features inflating complexity without adding predictive power
Cursor 2.0 (~2 hours):
- Functional forecasting system
- Less thorough documentation
- Did not beat MAPE benchmark (though respectable results)
What I Learned
Transparency matters. Cursor's visual interface made it easier to trust the process. Claude Code's terminal-only approach felt like launching a missile and hoping.
Specialized agents are powerful. Claude's ability to spawn domain-expert agents mid-project is a game-changer. It's like having a consultancy you can pull in as needed.
Domain knowledge still wins. Both agents failed my trap until I pointed it out. They executed forecasting mechanics beautifully but lacked the awareness to question whether they were doing the right things. The gap between "technically correct" and "statistically rigorous" is where human expertise dominates.
AI assistants, not replacements. These tools accelerate work I already know how to do. They're less valuable as autonomous decision-makers on complex technical problems. The PhD Statistician agent was amazing, but I had to know to call for it.
The Verdict
If choosing one tool for autonomous data science work, I'd pick Claude Code, but with reservations.
Claude Code wins on: Speed, specialized agent flexibility, output quality
Cursor 2.0 wins on: User experience, transparency, iterative collaboration
The real takeaway? We're entering an era where the question isn't "can AI do data science?" but "how do I best collaborate with AI to do better data science?"
