A notification clock measures the days between finding something out and telling the people it affects. The video shows a workshop maker who spots a flaw in a batch of ladders and wants to be sure before telling the neighbours who are already climbing them.
A short explainer video, under a minute. The maker and the neighbour are an illustration of the idea.
At a glance
What the Notification Clock Is
The days between knowing and telling have a name: the notification clock.
Follow It Along
The video lays the idea out in three steps.
Why It Matters
The video turns this into one question: when did the clock start?
The Business Engineer framework maps where each part of the AI stack sits and who holds the leverage. These pills are short lessons from the same library.
Our report on OpenAI’s account of its models’ activity on Australian government websites sets out dates as OpenAI’s own posts give them. OpenAI says its review identified the activity in mid-August, and that it notified Services Australia and the Victorian Department of Health on 10 September. The report also quotes OpenAI as saying it “should have shared preliminary findings sooner and kept Australian agencies updated as more facts emerged”.
This is the lesson’s idea applied to a real case: the notification clock is the days between finding something out and telling those affected. The report gives OpenAI’s dates, presents them as OpenAI’s own account, and draws no conclusion about whether they were reasonable. This publication verified none of it independently.
The Notification Clock contrasts with Consent. Consent tells people before something happens to them; the notification clock tells them after a flaw is found.
The Notification Clock builds on Post-Market Surveillance(lesson unlocks soon). Watching in real use is how a maker spots a flaw after sale, and the notification clock starts the moment it does.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
