Jellyfish reports AI spend and throughput from aggregate, industry-level research. Becoming AI-native gets answered one org and one engineer at a time: how well the harness behind a session performed, and whether the engineer had a way to keep sharpening both. Tetriz reads that layer directly, inside one org.
Jellyfish's AI Engineering Trends study, run with Harvard researchers, is a credible industry-level benchmark: a real, reassuring finding that AI adoption lifts throughput without an apparent aggregate quality hit. It's still a cross-company average. It can't say which team or session is driving the pattern inside one specific organization, in either direction. Tetriz's session data is built to operate at that resolution.
A platform built for CFO-facing R&D capitalization and board reporting is answering a finance question. It says nothing about whether the AI sessions behind that spend were well-directed. Tetriz reads the session directly. Is the spend defensible? Is the AI being used well? Each question gets a separate, session-grounded answer.
The Tetriz Desktop App gives every engineer a personal coaching view (session efficiency, focus time, prompt patterns) independent of any team or org rollup, with no minimum team size to get started.
Not usually. Teams that value Jellyfish's R&D capitalization and board-reporting layer keep it for finance translation, and add Tetriz for the AI-session layer underneath.
The Tetriz Desktop App on each engineer's machine, plus admin-authorized access to GitHub, GitLab, or Bitbucket for pull-request correlation, with no minimum team size to get started.
No, and it doesn't need to. That research is a useful industry-level benchmark: aggregate adoption and throughput across the industry. It can't say how well any one org's sessions and the harness behind them actually performed, or whether its engineers had a way to keep sharpening both, the layer that decides whether that org becomes AI-native. Tetriz measures it, org by org.