Most businesses today can see how much AI costs, but still struggle to understand what it actually delivered. Token bills tell us what the machine consumed, not whether the work created value. That gap is what we're trying to address with quirq, a unit I’m working on to measure verified business impact from Agentic work. The future I see is not one where AI replaces human accountability or talent, but one where humans stay accountable for defining the outcome, deciding what it is worth, and setting the budget. AI then goes and executes the work, while the environment verifies what was actually done, what it cost, and where humans still had to step in. This becomes especially important as companies move from AI demos to actual agentic workforces. My belief is that the next phase of AI adoption will not just be about better models, but better environments that can prove what work was done, what it was worth, and whether the system is getting better over time. We wrote a paper addressing this problem and proposing a new way to measure the business impact of agentic work. Sharing an early draft and would love feedback, criticism, and sharp counterarguments.
