1. What is "wage scarring" as documented by von Wachter, Song, and Manchester (2009)?
Correct. Wage scarring describes the documented, long-lasting earnings reduction that follows mass displacement — particularly severe when displacement occurs after age 50 and during economic downturns. Von Wachter et al.'s work using Social Security Administration data showed these effects persisting across the remainder of affected workers' careers.
Not quite. Wage scarring refers to the persistent long-term earnings penalty that follows mass layoff or displacement — documented extensively by von Wachter et al. using Social Security data. The effect is largest for older workers and those displaced during recessions, and it rarely fully recovers over the remainder of the affected career.
2. Denmark's flexicurity unemployment benefits replace what percentage of previous wages, for what maximum duration?
Correct. Danish UI replaces up to 90% of previous wages (capped at ~DKK 19,000/month) for up to 2 years — a replacement rate and duration dramatically higher than any U.S. state program, enabling genuine career transitions rather than emergency bridging.
Denmark's UI replaces up to 90% of previous wages for up to 2 years. Compare this to the U.S. average of 43% replacement for up to 26 weeks — the gap illustrates why flexicurity enables real career transitions while U.S. UI mainly bridges emergency periods.
3. Justin Welsh's documented $5 million+ in annual revenue as a solo operator was primarily driven by which asset?
Correct. Welsh's $5M+ revenue was built on his 400,000+ LinkedIn and newsletter audience — the AI tools amplified his output capacity, but the audience was the primary asset.
Not quite. Welsh's revenue was primarily driven by his 400,000+ LinkedIn followers and newsletter subscribers — the distribution was the core asset.
4. The evidence-signaling approach to career positioning differs from credential signaling in what fundamental way?
Correct. Evidence signaling demonstrates capability through documented outcomes — specific results, quantified impact, AI context — while credential signaling only asserts capability through a certificate or degree. In transition markets where AI credentialing lags adoption, evidence outperforms credentials.
The core distinction is demonstration versus assertion: evidence signaling shows specific documented outcomes while credential signaling asserts capability without direct proof. Evidence is more effective in markets where formal AI credentialing lags actual adoption.
5. Which of the following best defines "asymmetric leverage" as used in the context of AI-native businesses?
Correct. Asymmetric leverage is when a small AI-enabled team serves markets and produces output volumes that historically required much larger organizations.
Not quite. Asymmetric leverage means a small team using AI can produce outputs at a scale historically requiring much larger organizations.
6. In what year did the FDA clear IDx-DR — the first AI authorized to provide a clinical diagnosis without a clinician's direct involvement?
Correct. The FDA cleared IDx-DR in 2018 for diabetic retinopathy screening in primary care — the first AI diagnostic authorized without clinician involvement.
Incorrect. IDx-DR was FDA cleared in 2018, the same year DeepMind published its Moorfields eye disease study.
7. In a December 2023 analysis of Product Hunt launches, products from founders with 10,000+ Twitter followers received how many times more upvotes than equivalent products with no prior audience?
Correct. The analysis found products from founders with established audiences received 7× more upvotes on Product Hunt.
Not quite. Products from founders with established Twitter audiences received 7× more upvotes on Product Hunt.
8. What does the EU AI Act require specifically for AI systems used in employment decisions such as hiring, firing, and performance management?
Correct. The EU AI Act classifies employment AI as high-risk and requires it to be documented, auditable, subject to human oversight, and developed with consultation from worker representatives — no equivalent exists in current U.S. federal law.
The EU AI Act requires high-risk employment AI systems to be documented, auditable, subject to human oversight, and developed with worker representative consultation — a significantly more protective regulatory posture than exists in current U.S. federal law.
9. Which of the following is NOT one of the four layers in an AI operations stack as described in Lesson 3?
Correct. The four layers are creation, automation, analytics, and customer. There is no "hiring layer" — the premise of the lean AI model is that AI replaces many functions that previously required hiring.
Not quite. The four layers are creation, automation, analytics, and customer. "The hiring layer" is not one of them.
10. The Burning Glass 2023 finding on "relationship management" in job postings showed it appeared how much more frequently than in 2019?
Correct. Burning Glass found "relationship management" appeared in 38% more job postings in 2023 than in 2019 — accelerating as AI absorbed transactional communication, making interpersonal influence a more visible and premium skill.
Burning Glass found a 38% increase in "relationship management" mentions in job postings from 2019 to 2023 — as AI absorbed transactional communication, interpersonal influence became a more explicitly demanded skill.
11. The 2016 ProPublica analysis of COMPAS revealed that the system's primary limitation was its inability to:
Correct. COMPAS was internally consistent but structurally unable to exercise the contextual judgment that would identify when historical data reflected discriminatory policing or when individual circumstances had changed meaningfully.
The issue was not technical capacity or consistency. COMPAS produced consistent outputs from biased data and could not exercise the contextual judgment to recognise that limitation.
12. Finland's UBI experiment found that compared to the control group, recipients had what primary advantage?
Correct. Kela's results showed wellbeing and mental health as the strongest gains, with modest but positive employment effects (6 additional working days). The program was not expanded due to cost concerns despite positive outcomes.
Finland's primary finding was wellbeing-centered: recipients showed substantially better mental health, institutional trust, and confidence in the future, with modest employment gains (6 additional days worked). The program was not extended due to cost.
13. Iceland's 2015–2019 4-day work week trial (covering ~1% of the workforce) found what primary result?
Correct. Iceland's trial — results published in 2021 — found maintained or improved productivity in nearly all participating workplaces, with substantially better worker wellbeing. The experiment contributed to negotiated changes in working hours across Icelandic labor agreements.
Iceland's trial (2015-2019, published 2021) found productivity maintained or improved in nearly all workplaces, with substantially better worker wellbeing. The results were strong enough to influence actual labor agreements — not just academic interest.
14. The iCAD ProFound AI mammography tool, FDA cleared in 2019, achieved what combination of outcomes in published clinical trials?
Correct. ProFound AI acted as a second reader — improving detection while simultaneously reducing the time radiologists spent per study.
Incorrect. ProFound AI's published trial outcomes were an 8 percentage point sensitivity improvement and 52.7% reading time reduction — acting as a second reader alongside radiologists.
15. Which of the five AI-suitability markers did JPMorgan's COIN system satisfy that made it a success?
Correct. COIN's success followed the AI-suitability profile precisely: structured inputs, predefined targets, high volume, clear correctness standard.
COIN succeeded because contracts are structured and target clauses are predefined — the opposite of creative, unstructured, or low-volume work.
16. Which type of skill has the longest half-life and typically appreciates in value as AI absorbs routine cognitive work?
Correct. Foundational cognitive skills — critical thinking, synthesis, judgment under uncertainty — decay slowly and become more valuable as AI handles routine cognitive tasks.
Foundational cognitive skills have the longest half-life and can appreciate in value as AI handles more routine work.
17. How does Germany's apprenticeship system's per-capita participation compare to the United States?
Correct. The U.S. had ~593,000 registered apprentices in 2022 — roughly one-sixth Germany's per-capita rate — and 70% concentrated in construction trades, limiting coverage of the expanding AI-vulnerable white-collar workforce.
U.S. apprenticeship participation is approximately one-sixth of Germany's per-capita rate, and 70% concentrated in construction. Germany's system covers 325 occupations; the U.S. system is far narrower and shallower in its reach.
18. What is "context collapse" as a failure mode in lean AI operations?
Correct. Context collapse occurs when AI tools lack the accumulated institutional knowledge and relationship context that human team members carry — causing errors that damage customer relationships.
Not quite. Context collapse is when AI-handled interactions lack the institutional knowledge and relationship context that human team members accumulate over time.
19. Margaret Boden's concept of transformational creativity differs from combinational creativity in that it:
Correct. Transformational creativity breaks the existing paradigm rather than extending it — it generates new conceptual spaces rather than filling existing ones. This is the form of creativity observationally rare in AI output.
Transformational creativity is not about speed, volume, or data. It fundamentally restructures the space in which creativity operates — generating new paradigms rather than new examples of existing ones.
20. "Trust calibration" as defined in Lesson 3 refers to:
Correct. Trust calibration is the individual skill of knowing when to act on AI output and when to verify it — identified as a critical competency in the BCG study.
Trust calibration is the individual skill of accurately judging when AI outputs are reliable versus requiring verification — a key competency in effective human-AI collaboration.