AI Economy Quiz: How Ready Are You for the Transformation Ahead?

What does measuring AI ROI require BEFORE deployment?

AI ROI is always calculated the same way: cost of tool license divided by number of users AI ROI should only be measured for revenue-generating applications, not internal productivity tools ROI can only be measured after 12 months of operation; pre-deployment metrics are not useful Establishing clear, measurable success criteria — specific metrics like time savings, error rate reduction, or volume increase — so that result can be assessed against baseline after deployment

For a worker in a high AI substitution risk role, what is the most effective adaptation strategy?

Advocate for regulation that bans AI in your industry Avoid using AI tools entirely so that your role remains necessary Upskill into adjacent roles with more human judgment content — a data entry clerk learning SQL and data analysis moves into an AI-augmented growth role rather than a declining substituted one Specialize more deeply in the exact tasks AI is replacing to become the best human doing those tasks

Which statement about AI API output tokens is correct?

Input and output tokens are always priced identically at major providers Output tokens are not counted toward API costs at the Sonnet/GPT-4o tier Output tokens are typically more expensive per token than input tokens Output tokens are always cheaper because the model is just retrieving stored answers

A company plans to run a customer service AI chatbot handling 500,000 short conversations per month. Which cost dimension should they optimize first?

Use a smaller, faster, cheaper model (e.g., Haiku or Flash tier) since the tasks are short and high-volume Always use the highest-capability model to ensure quality Choose the model with the largest context window Pick the model with the best score on the MMLU benchmark

What is meant by 'distributional tension' in the context of AI economic gains?

Distributional tension refers to AI models that provide uneven answer quality AI creates equal productivity gains for all workers regardless of skill level AI productivity gains are initially concentrated among AI company shareholders and skilled tech workers, raising concerns about inequality before benefits spread broadly AI companies are required to distribute profits to all affected workers

Why is maintaining deep domain expertise emphasized alongside developing AI fluency, rather than prioritizing one over the other?

Domain expertise is no longer valuable once AI can perform most knowledge work tasks AI fluency is sufficient — domain expertise is what AI provides from its training data AI fluency without domain expertise produces volume without quality; domain expertise without AI fluency loses productivity to AI-augmented competitors. The combination — T-shaped AI-fluent experts — produces the highest-quality output at scale Only AI engineers need domain expertise; most professionals only need basic AI tool fluency

What is the difference between 'high AI exposure' and 'high substitution risk' for a role?

High exposure means AI significantly affects the tasks in a role; it may complement the worker (boosting productivity) or substitute for them. Risk depends on whether AI augments or replaces the majority of tasks High exposure always means high risk — any role AI touches is at risk of disappearing Substitution risk refers only to blue-collar roles; white-collar roles only face exposure High exposure and high substitution risk mean the same thing

Which AI capability allows a small business to maximize the reach of a single piece of content?

Using AI to repurpose one piece of content into multiple formats — blog to social posts, email, FAQ, LinkedIn article Using AI to automatically post the same content to all platforms simultaneously Having AI scrape competitor content and rewrite it AI translating content into multiple languages automatically

What is the NIST AI Risk Management Framework and who is it intended for?

A voluntary best-practice framework from the US government's technical standards body, designed for any organization deploying AI — providing structured guidance for identifying, assessing, and managing AI-related risks NIST's framework is mandatory for all US businesses using AI NIST's AI framework is a certification that AI products must earn before selling in the US The NIST framework applies only to government agencies and defense contractors

What is the key distinction between task displacement and job displacement in AI labor economics?

Task displacement is temporary; job displacement is permanent Task displacement means AI takes over specific task types within a job; job displacement means entire roles become obsolete — the former is far more common Task displacement affects blue-collar work; job displacement affects white-collar work There is no meaningful distinction — all task displacement eventually leads to job displacement