Code-only AI for data science does not work. Period. Context aware tools are the only way data scientists get real work done with AI.

I have worked with products that looked sharp in a demo but collapsed in real workflows. They generated code but ignored the sequence of steps, the data in play, and the questions being asked.

That is not data science. That is gambling with disconnected outputs. I have seen how that leads to drift, wasted time, and broken trust. No one wants that.

Why context matters in data science

Data science depends on context. Each step informs the next: queries, transformations, models, evaluations. If your tools only see the current cell, they are blind to the process that got you there. That is how small mistakes turn into larger failures.

Context aware AI fixes this. It carries knowledge forward across the workflow. It knows the path you took, the datasets in use, and the assumptions you set. Suggestions stay relevant because the tool has the same memory you do. That lets you test faster, refine quicker, and avoid repeating yourself.

Teams cut wasted cycles when tools carry context forward instead of resetting at every step. That is the difference between AI that spits out code and AI that understands data science.

The inspiration behind Zerve

This is why we built Zerve. We were tired of tools for software development being forced into data science. They look like they work on public or toy datasets and leave a gap when the data is real.

Zerve is an environment built for data science. Interactive and iterative. It does not stop at generating a snippet. It keeps track of the workflow, the assumptions, and the iterations. It acts like a teammate who remembers what has been tried, what worked, and what failed. That memory compounds.

If you take one thing away, let it be this. Tools that forget context waste your time. Tools that keep it move the work forward.