Research & Evidence Library
Grounded summaries of peer-reviewed literature, field experiments, and empirical measurement on AI productivity.
When Should You Actually Pay for AI?
A Decision Threshold Framework for Indian Professionals & Knowledge Workers
This foundational research paper synthesizes empirical evidence from major field trials to establish when AI subscription upgrades can generate genuine surplus value. It underpins the Payoff Signal VALUE methodology and our AI subscription decisions.
Underlying Empirical Studies
We rigorously isolate what studies actually observed from marketing generalizations.
Noy & Zhang, Science (2023)
Sample: 453 professionals performing bounded writing tasks- Reported ~40% lower completion time for tested writing tasks
- Reported ~18% higher human-rated output quality
- Lower-performing participants benefited disproportionately more
Do not generalize this bounded laboratory writing result to all complex, iterative professional workflows.
Dell'Acqua et al. / Harvard & BCG Field Experiment (2023)
Sample: 758 management consultants- For tasks strictly inside the AI capability frontier, participants completed 12.2% more tasks, 25.1% faster
- Quality rated >40% higher for tasks within the frontier
For tasks outside the capability frontier ("jagged frontier"), AI users were 19 percentage points less likely to produce correct solutions.
Brynjolfsson, Li & Raymond / NBER (2023)
Sample: 5,179 customer-support agents- Average issue-resolution productivity improved ~14%
- Gains concentrated heavily among novice and low-skill workers (up to 34% gain)
- Minimal impact on top-quartile experienced workers
Do not imply that customer-support chat gains apply uniformly to independent knowledge workers or consultants.
Microsoft / NBER Knowledge-Worker Experiment
Sample: 7,137 knowledge workers across 66 enterprise firms- Treated users showed approximately 2 fewer email hours per week
- Slight increase in asynchronous collaboration
Broader detectable work-pattern and overall productivity changes across enterprise metrics were statistically limited.
Cui et al., Management Science (2026)
Sample: 4,867 software developers across Microsoft, Accenture, and an anonymous Fortune 100 firm- Combined estimate showed approximately 26.08% more completed tasks (standard error 10.3%)
- Less-experienced developers demonstrated greater adoption and higher percentage gains
Completed tasks are not identical to delivery time or software quality. Individual company trials were noisy and varied, and the study was provider-affiliated.
Peng et al. / GitHub & Microsoft Research (2023)
Sample: 95 recruited software developers implementing an HTTP server in JavaScript- Copilot-assisted group completed the bounded task 55.8% faster than the control group
- Task completion rate was 78% for the Copilot group vs. 70% for the control group
Evaluated one constrained greenfield task with visible tests using an early Copilot version. This provider-affiliated laboratory result cannot be generalized to production repositories.
METR Research (2025, updated Feb 2026)
Sample: 16 experienced open-source developers completing 246 tasks in mature repositories- Participants took approximately 19% longer when early-2025 AI tools (primarily Cursor Pro with Claude 3.5/3.7 Sonnet) were permitted
- Developers expected speedups despite measured delays in the early-2025 trial
The early-2025 slowdown occurred in familiar mature codebases. METR's February 2026 update noted later signals pointing toward speedups, but reported that participant and task selection, a lower pay rate, and concurrent-agent time measurement made the magnitude unreliable—considering the later estimate a weak signal rather than a dependable current effect size.
Demirer, Musolff & Yang (2026 revision)
Sample: Matched event study of over 500,000 GitHub developers- Cumulative activity increased substantially for commits (up to 240% for autonomous agents)
- Gains attenuated sharply down the delivery pipeline to 80% for projects and 30% for releases
Observational event study using internal Microsoft telemetry and public GitHub data; authors disclose past Microsoft Research affiliations. Shows that activity gains attenuate before production release.
Fu et al. (2025 revision; first posted 2023)
Sample: 733 code snippets attributed to AI assistants in GitHub repositories- Identified security weaknesses in 29.5% of sampled Python snippets and 24.2% of JavaScript snippets
- Demonstrated common CWE patterns in generated code across multiple assistants
Covers older tool generations and static-analysis criteria across mixed assistants; does not establish current Copilot vulnerability rates, but confirms why security and defect review belong in workflow costs.
Liu, Zhang & Liang, Findings of EMNLP (2023)
Sample: Human evaluation across 4 generative search engines- Across audited systems, only 51.5% of generated sentences were fully supported by citations
- Only 74.5% of citations supported their associated sentence
Evaluated 2023 systems, not current product versions. Fluent prose with inline citations can still contain unsupported claims; verification remains an essential cost.
Xu, Feng & Chen (2023)
Sample: Randomized online search experiment comparing chatbot and search engine- Chatbot group spent less time on tested tasks with no significant difference in overall task performance
- Chatbot users performed less well on fact-checking tasks
Observed in a bounded experimental comparison of earlier tools; does not prove a current subscription ROI or universal productivity effect.
Wadinambiarachchi et al., CHI (2024)
Sample: 60 participants in a between-participants visual ideation task- Exposure to AI-generated images increased fixation on an initial example
- Participants using the AI image generator produced fewer ideas with less variety and lower originality
Evaluated one visual ideation experiment; not a test of Canva, Canva Pro, or commercial production speed. Supports charging for human review rather than assuming more generations equal more value.
Fu et al. (2024 preprint)
Sample: 36 participants producing 105 evaluated advertisement designs- Evaluators rated GenAI-supported designs as more creative and unconventional
- Found no statistically significant differences in visual appeal, brand alignment or usefulness
Controlled experiment with five condition-blinded evaluators drawn from the research group being advertised; exploratory subgroup findings. Not a Canva Pro field study; supports measuring approved, useful designs rather than novelty.
Doshi & Hauser, Science Advances (2024)
Sample: Online short-story writing experiment across recruited participants- Access to generative-AI ideas improved individual evaluations, especially for less creative writers
- AI-assisted stories became collectively more similar to one another
Short-story writing experiment, not visual design, Canva or subscription ROI. Shows that individual assistance can coincide with collective standardisation.
Astaño, Meta-Analysis (2025)
Sample: 22 studies and 26 datasets (n = 1,540) on Canva-supported instruction- Reported large pooled association between Canva-supported instruction and student academic performance
- Found substantial heterogeneity and evidence of publication bias across included records
Studies were mostly from Indonesia and of moderate quality. Shows educational utility of Canva, but does not isolate Canva Pro, prove causality across all contexts, or justify paid subscriptions for educators eligible for Canva Education.
Anthropic Economic Index Productivity Estimate (Provider-Produced, Model-Estimated)
Sample: 100,000 anonymized Claude.ai conversations analyzed using Claude- Model-estimated task duration with and without AI produced an estimated average 80% time reduction for observed tasks inside the conversation
- Observational, provider-produced analysis of Claude.ai transcripts rather than a controlled human timing trial
Provider-produced, model-estimated observational research. Anthropic explicitly notes this method cannot count human validation or accuracy checks outside the conversation and may overstate current productivity effects. It is not a Free-versus-Pro experiment and must not be used as a causal productivity multiplier.
An economic model applying writing, consulting, developer and provider-estimated evidence boundaries to Claude Free and Pro.
An economic model applying these developer productivity findings to Free, Pro, Pro+ and Max tiers.
Future Research
More Payoff Signal research will be added as new decisions are investigated.