In one tech team, both developers and analysts use AI assistants. They soon discover, however, that AI alone doesn’t guarantee success—the winners are those who, besides IQ, also bring in EQ (collaboration), AQ (adaptability), and CQ (cultural sensitivity). In the age of AI, it’s the interplay of abilities that wins, not a single score.
Terms and what they measure: IQ, EQ, AQ, CQ
IQ is general intelligence—the ability to think logically and solve problems (measured, for example, by WAIS or Raven’s Progressive Matrices). EQ denotes emotional intelligence: the ability to perceive and regulate one’s own and others’ emotions (tests such as MSCEIT or the TEIQue questionnaire). AQ, the adaptability quotient, expresses resilience and flexibility in the face of change (it can be measured, for instance, by the CD-RISC resilience scale). CQ is cultural intelligence—the ability to function across cultures (assessed by the CQS questionnaire). It’s also worth mentioning grit(perseverance) and conscientiousness (traits linked to persistence). Each of these metrics has limits—test reliability varies, tasks may favor certain cultural backgrounds, and a single score never captures a person fully.
Why these capabilities matter today
Automation and AI are taking over routine tasks, which raises the value of abilities like problem definition, critical thinking, learning, and collaboration. For example, according to the World Economic Forum, 50% of employees will need reskilling by 2025. Analyses suggest that by 2030 demand for socio-emotional skills will rise by about 25%, and critical thinking with problem solving remains at the top of in-demand skills. A combination of IQ + EQ (and also AQ and CQ) prepares developers or data scientists better for performance than narrow technical knowledge alone. Available data link higher emotional intelligence with better job performance and healthier relationships.
AI: amplifier of talent, or a crutch?
Modern technologies can act as an amplifier of our intelligence—but also as a comfortable crutch we lean on too much. Cognitive offloading refers to shifting mental tasks to external sources. A typical example is GPS navigation: those who rely on it all the time see their natural sense of direction weaken. Similarly, the Google effect describes the finding that we remember information less well when we know it’s easy to find online—the brain tends to remember where to find facts rather than the facts themselves. Conversely, some see AI and tools as an extended mind—used well, AI lets us tackle tasks faster and at greater scale. We must, however, beware of autopilot. Blind trust in AI leads to automation bias, i.e., errors from automatically relying on the machine. There’s also the risk of skill fade: abilities we don’t practice gradually atrophy. The golden rule: “Copilot, not autopilot.” AI should assist, not decide for us. Turn AI off when learning new things or when you need to grasp a problem in depth, and turn it on for inspiration, variant generation, or routine review. Used this way, technology will truly sharpen our minds instead of lulling them to sleep.
Quick tip: Try to retrieve new information from memory or solve a problem with your own reasoning first, without a search engine or AI. Only after you’ve tried on your own, verify your answer with technology.
Labor market and global comparison
Across fields, demand is growing for the so-called skills of the future: critical thinking, the ability to learn new things, collaboration (often virtual), and digital literacy—including working with AI. Regions differ in strengths according to their education systems and conditions. For instance, adults in Finland or Japan excel at problem solving in international tests, while other places emphasize different skills. These differences are not due to innate differences. Estonia (a top performer in PIAAC) shows relatively small gaps in skill levels across socio-economic groups—equal educational opportunities can develop talent broadly across a population. A common trend is that people who keep learning and combine expertise with soft skills succeed everywhere. GitHub, for example, reports that 92% of developers worldwide are experimenting with AI assistants—which underscores the importance of AI literacy (the ability to use AI effectively). Those who can work with AI while not neglecting IQ, EQ, and other capabilities will have an edge in the global labor market.
How to measure capabilities in practice
A trustworthy picture of someone’s capabilities comes from combining methods. Supplement standardized tests (IQ, EQ, etc.) with 360-degree feedback (peer evaluations) and performance indicators (work outcomes). It’s also worth tracking how people work with AI—for example, the share of time spent working independently vs. with AI support. Keep ethics in mind: test results are sensitive and should serve development, not labeling people.
How to develop capabilities
IQ (thinking): Train your brain on complex tasks. First try to solve the problem yourself (without AI), then compare your approach with AI’s advice—this leverages the generation effect and strengthens memory. Don’t underestimate desirable difficulties—space out your learning over several days and occasionally test yourself by retrieving knowledge from memory, even if it’s hard.
EQ (emotions): Meditate or calm the mind for a few minutes daily to boost self-awareness. Rehearse difficult conversations (AI can role-play) and regularly ask colleagues for feedback—it improves empathy and communication. Consider keeping an emotions journal—it helps reveal patterns and progress in handling situations.
AQ (adaptability): Regularly expose yourself to small uncertainties—take on a task with no clear outcome. After each setback, constructively analyze what to try differently next time (without blaming yourself or others). With AI, you can also simulate “what-if” scenarios and prepare responses in advance.
CQ (cultural intelligence): Work with people from different backgrounds—join international projects, consider short rotations, or at least online collaboration across cultures. Learn languages and local customs; true understanding comes through dialogue and listening. A translator or AI assistant can help you orient quickly—but it gives simplified averages; you’ll grasp real nuance in conversations with locals.
Metric — What it measures — Example tool — When to use in practice
IQ — General cognitive abilities, logical and abstract thinking — WAIS-IV, Raven’s Matrices — Assessing the ability to tackle complex, novel problems (e.g., in R&D roles).
EQ — Self-awareness, empathy, emotion regulation, social skills — MSCEIT, TEIQue — Leadership development, improving team collaboration, conflict resolution.
AQ — Resilience, ability to adapt to change and uncertainty — CD-RISC — Strengthening teams during transformation, building stress resilience.
CQ — Ability to function effectively in culturally diverse settings — CQS (Cultural Intelligence Scale) — Preparing for global teams, international expansion.
Conscientiousness — Reliability, organization, persistence (Big Five trait) — BFI (Big Five Inventory) — Predicting long-term performance across most roles.
Mini case studies
Developer: Follows the rule “me first, then AI”—first designs tests and a solution alone, then uses AI for code review; this preserves sharp thinking and lets AI multiply effectiveness. The result is higher-quality code and mental agility that persists.
Product manager: Breaks down an ambiguous task into parts and asks AI to surface risks, but crafts the final strategy with a cross-continent team (leveraging CQ). AI provides ideas; she critically filters and adapts them to reality. The plan is more robust and she can face uncertainty.
Self-taught language learner: One month entirely without AI, the next with AI as a conversation partner; the combination of independent practice and instant feedback produced the biggest gains.
Toolkit & checklist
Measure yourself: Do a baseline test (IQ/EQ) or gather feedback, and set your AI-on-task ratio (share of time without vs. with AI).
Daily routine: Include one no-AI work block (deep focus offline) and one AI-copilot block (a creative or difficult task with AI) every day.
15-minute training: Practice a chosen skill daily for 15 minutes. For memory, for example—try to learn or draft something without hints, then check and learn with AI.
Weekly reflection: Review mistakes and wins once a week. Note where AI helped or hurt, and pick a small improvement target (e.g., better listening).
After 3 months: Repeat tests or assessments and track progress. More important than the numbers is feeling more competent and less dependent on crutches.
Controversies and limits
One test doesn’t say it all. Remember that neither IQ nor EQ scores define a person’s worth. Intelligence tests have historically been affected by cultural bias and can still advantage those familiar with such tasks from an early age. Results are also influenced by current state—fatigue, stress, stage fright. Risk of “deskilling.” If we rely only on technology, we can lose valuable abilities. It’s crucial to build environments that support learning, curiosity, and human engagement. Attempts to shift decisions purely to algorithms (e.g., in hiring) show that machines can filter out talent due to biased data or missing human context. No test or AI fully captures what makes a person unique. Not everything can be measured by numbers—motivation, creativity, and moral values matter too.
Conclusion
We can liken AI to a lever: it can multiply the strength of our minds, but by itself it does nothing—it needs our hand. Our IQ, EQ, AQ, and other abilities are the fulcrum—the stronger they are, the heavier a “load” we can lift with the lever (AI). Each of us can become a better version of ourselves by developing these abilities and complementing them wisely with technology. So choose one metric to track, one technique to develop, and one new AI-work habit, and stick to them for the next 30 days. You’ll see that technology won’t dull you—on the contrary, it will bring out the best in you.