Standard AI tests cannot tell if a model can handle the messy, personal tasks people truly value, like moving apartments or managing their finances. LifeVal was created by surveying thousands of consumers on what they would pay an assistant to do, then building test tasks from real user data and personal context, with strict quality controls. The result is a grounded in consumer value that can objectively grade highly subjective and personalized tasks, giving developers a meaningful measure of an assistant's real world utility.
AI assistants face a $1.7 trillion wish list of life tasks
Yang and colleagues introduce a benchmark of personalized consumer tasks, drawing on a survey estimating more than $1.7 trillion in annual willingness to pay for digital assistance.
Big Tech
Jenny Yang · Elizabeth Proehl · Anjan Bharadwaj · Emily Fan · Charlotte Cole · James Kim · +4 more
OpenAI
Research Digest··2 min read
In one line
LifeVal benchmarks AI models on high value, personalized, agentic consumer tasks rooted in real user data.
Before you read this
Three ideas this paper builds onbenchmarkA standard set of tasks or data used to evaluate and compare the performance of AI systems.310 papers →agentic evaluationMeasuring AI agents' performance in complex tasks, highlighting failures in planning, monitoring, and real267 papers →verificationChecking outputs for correctness before accepting them, often to catch errors or reward hacking.297 papers →
Read it as
Yang and colleagues introduce LifeVal, an evaluation benchmark built around consequential personal tasks such as managing finances, moving apartments, finding childcare and searching for work.
The authors surveyed a demographically representative sample of more than 3,800 consumers about willingness to pay for a personalized digital assistant.
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