AI Skills for Founders vs Engineers [Data]

AI skills for founders look remarkably similar to engineering skills at the composite level — but the dimension-level data tells a different story. Across 715 completed AISA assessments (225 founders, 490 engineers), founders and engineers land within half a point of each other on overall AI fluency, yet diverge meaningfully on critical thinking, safety awareness, and — most strikingly — how accurately they predict their own scores.

This post breaks down the dimension-by-dimension comparison, explains the self-awareness gap, and outlines what the data means for hiring decisions and team composition.

Composite Scores: Closer Than You'd Expect

Founders and engineers are nearly identical in overall AI fluency. Founders average a 55.3 composite (n=225) versus engineers at 54.8 (n=490). Both sit squarely in the Developing tier (28–59), above the platform-wide average of 46.3 across all 2,399 assessments.

That 0.5-point gap is noise. If you stopped at the composite, you'd conclude these two groups are interchangeable in AI capability. But composites compress five distinct dimensions into a single number, and the compression hides real differences.

The World Economic Forum's Future of Jobs Report 2025 found that AI and big data skills top the list of fastest-growing skills globally, with 92% of employers planning to adopt AI by 2030. Both founders and engineers are investing in these skills — but they're investing in different parts of the stack.

How Both Groups Compare to the Platform Average

For context, the overall AISA population (n=2,399) averages 46.3. Both founders and engineers outperform that baseline by 8–9 points. This isn't surprising — both roles tend to be early adopters with professional motivation to use AI tools daily. What matters is where each group's points come from.

Tier Placement Is Identical

Both groups fall in the Developing tier. Neither group's average reaches Proficient (60–79). This aligns with broader patterns we observe: even technically sophisticated professionals rarely cross the Proficient threshold without deliberate practice across all five dimensions of AI fluency.

Where Founders Lead: Critical Thinking and Safety

Founders outperform engineers in three of five dimensions: prompting, critical thinking, and safety. The gaps are modest but consistent, and they point to a specific pattern in how founders interact with AI.

Critical Thinking: Founders Ask Better Questions

Founders score 50.0 on critical thinking versus 48.3 for engineers — a 1.7-point gap. In AISA's rubric, critical thinking covers skills like claim decomposition, source verification, and recognising when AI output needs human judgment.

The pattern we observe in assessment transcripts is that founders tend to treat AI output as a draft to interrogate rather than a solution to implement. Engineers, particularly those accustomed to well-defined problem spaces, more often accept structured output at face value.

Safety: The Largest Gap Between the Two Groups

The 2.4-point safety gap (50.2 vs 47.8) is the widest dimension-level difference in this comparison. Founders score higher on questions about data privacy, responsible deployment, and understanding when AI use creates organisational risk.

This makes intuitive sense. Founders carry liability. They think about what happens when an AI-generated contract clause is wrong, or when customer data flows through a third-party model. Engineers are more likely to focus on whether the code works. Both perspectives matter — but the safety dimension captures a skill that founders practice more naturally.

AISA's safety dimension is also the lowest-scoring dimension platform-wide at 41.5, making the founder score of 50.2 notably strong relative to the broader population.

Prompting: A Narrow Lead

The prompting gap (52.0 vs 51.4) is small enough to be inconclusive on its own. Both groups demonstrate competent prompting skills, and the difference doesn't suggest a meaningful capability gap. What it does suggest is that founders aren't disadvantaged in the most visible AI skill — they hold their own against engineers who use AI tools as part of their daily technical workflow.

Where Engineers Lead: Technical Understanding

Engineers outperform founders on technical understanding by 2.0 points (51.1 vs 49.1). This is the one dimension where the engineering background provides a clear, measurable advantage.

Technical Understanding: Expected but Not Dominant

AISA's technical understanding dimension covers how models work — transformer architecture, token prediction, context windows, model selection, and the mechanics behind AI behaviour. Engineers score 51.1 here, founders 49.1.

A 2-point gap is real but smaller than many hiring managers assume. The expectation might be that engineers would dominate this dimension by 10+ points. They don't. Founders who actively use AI tools develop working mental models of how these systems behave, even without formal ML training. They may not explain attention mechanisms, but they understand context limits, model tradeoffs, and when to switch tools.

McKinsey's 2024 report on the state of AI found that 72% of organisations had adopted AI in at least one business function, up from 55% the year prior. As adoption spreads, technical understanding is becoming less of a specialist skill and more of a general leadership competency.

Workflow: Close, With Engineers Slightly Ahead

Workflow & Application scores are 55.6 for engineers versus 56.9 for founders. Founders actually lead here by 1.3 points. This dimension measures how people integrate AI into real work — task decomposition, multi-tool workflows, and knowing which tasks to delegate to AI versus handle manually.

Founders' slight edge in workflow likely reflects the breadth of their role. A founder might use AI for investor updates, competitive analysis, hiring rubrics, and product specs in a single week. That breadth builds workflow fluency faster than depth in a single domain.

The Prediction Gap: Founders Know What They Don't Know

This is the most striking finding in the data. When asked to predict their score before taking the assessment, founders overestimate by 7.3 points while engineers overestimate by 14.9 points — more than double the founder gap.

Note: prediction gap sample sizes differ from dimension samples because not all candidates complete the prediction step.

Why the Gap Matters More Than the Score

Confidence calibration — the ability to accurately assess your own competence — is a meta-skill that affects every decision you make about AI adoption. A leader who overestimates their team's AI capability will under-invest in training, set unrealistic timelines for AI-powered features, and miss risks that a more calibrated assessment would catch.

Founders' 7.3-point gap is the second-smallest of any role group in the AISA dataset (Product managers are closest at 8.4 points). Engineers' 14.9-point gap, while better than the platform average of 17.8, still represents a significant blind spot.

What Drives Engineer Overconfidence?

We observe several patterns. Engineers who work with AI tools daily often conflate tool familiarity with AI fluency. Knowing how to use GitHub Copilot effectively is a real skill, but it doesn't automatically translate to strong critical thinking about AI output or understanding of responsible AI deployment.

There's also a domain transfer assumption: "I understand software systems, therefore I understand AI systems." The AISA data suggests this transfer is partial at best. Engineers score well on technical understanding (as expected) but don't carry that advantage into safety, critical thinking, or even prompting — dimensions where software engineering experience provides less direct preparation.

For more on how engineering-specific patterns play out across a larger sample, see our analysis of 490 engineering assessments.

Founders' Self-Awareness Advantage

Founders tend to operate in environments of high uncertainty. They're accustomed to not knowing things and making decisions with incomplete information. This comfort with uncertainty appears to translate into more accurate self-assessment of AI skills.

The practical implication: when a founder says "our team needs AI training," they're more likely to be right about the gap. When an engineer says "I've got this covered," there's a higher probability of a blind spot — particularly in safety and critical thinking.

What This Means for Hiring and Team Composition

The founder-vs-engineer comparison isn't about declaring a winner. It's about understanding complementary strengths and building teams that cover the full AI fluency spectrum.

Stop Hiring for Composite Scores Alone

If you're using any form of AI skills assessment in hiring, look at dimension-level results, not just the top-line number. Two candidates with identical composites of 55 could have radically different profiles — one strong in technical understanding but weak in safety, the other the reverse. The right hire depends on what your team already has.

Pair Founders and Engineers Deliberately

The data suggests a natural complementarity:

• Founders bring stronger critical evaluation of AI output, better safety instincts, and more accurate self-assessment
• Engineers bring deeper technical understanding of model behaviour and mechanics
• Both share similar prompting and workflow capabilities

In practice, this means AI strategy decisions benefit from founder involvement (critical thinking, risk assessment), while implementation decisions benefit from engineering depth (model selection, architecture). Neither role covers all five dimensions alone.

Address the Engineer Prediction Gap Directly

A 14.9-point overestimation gap creates real organisational risk. Engineers who believe they're Proficient when they're actually Developing will resist training, skip verification steps, and deploy AI-generated code with less scrutiny than it deserves.

The fix isn't to tell engineers they're wrong — it's to give them evidence. An AI readiness assessment that produces dimension-level scores with evidence-linked feedback gives engineers the specific, technical feedback they respect. Abstract claims about "needing to improve" won't land. A score of 47.8 on safety with specific transcript evidence showing where they missed a data privacy consideration will.

For a deeper look at how to assess AI skills in interviews, we've written a separate guide on structuring that process.

Build Team Baselines Before Training

The most common mistake we see in AI change management is buying training before measuring. If your engineering team averages 51.1 on technical understanding but 47.8 on safety, a generic "AI fundamentals" course wastes time on what they already know and skims past what they actually need.

AISA for Teams produces team-level dimension breakdowns that make training investment decisions concrete. You can see exactly where your founders and engineers diverge and target development accordingly.

How to Use This Data

If you're a founder reading this, your likely development priority is technical understanding — closing the 2-point gap with your engineering team so you can participate more effectively in model selection and architecture decisions. The How AI Works guide in the AI Fluency Academy is a good starting point.

If you're an engineer, your likely development priorities are safety and critical thinking. The Responsible AI Use and Critical Thinking with AI guides address these directly.

If you're a hiring manager or VP of Engineering, the actionable takeaway is: don't assume your engineers are more AI-fluent than your founders. The composites are nearly identical, and founders bring strengths in dimensions that engineers tend to underweight. Build your AI skills assessment strategy around dimensions, not roles.

The prediction gap data also suggests that self-reported AI skill levels — whether on resumes, in interviews, or in internal surveys — should be treated with caution, especially from technical roles. Evidence-based assessment produces more reliable baselines than self-assessment, and AISA's 98% test-retest stability means those baselines hold up over time.

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Related reading: AI Skills for Engineers: What 490 Assessments Show — dimension-level breakdown of engineering AI fluency patterns.

Related reading: AI Safety Skills: The Lowest Score — why safety is the hardest dimension across all roles.

Related reading: How to Assess AI Skills in Interviews — structuring AI skill evaluation beyond self-report.

Frequently Asked Questions

Are founders better at AI than engineers?

Not overall. Founders average 55.3 on AISA's composite score versus 54.8 for engineers — a negligible difference. However, founders outperform engineers on critical thinking (+1.7 points), safety (+2.4 points), and workflow (+1.3 points), while engineers lead on technical understanding (+2.0 points). The two roles have complementary AI skill profiles rather than a clear hierarchy.

Why do engineers overestimate AI skills more?

Engineers overestimate their AI fluency by 14.9 points on average, compared to 7.3 points for founders. The pattern we observe is that engineers often conflate daily tool usage (e.g., code completion, debugging assistants) with broad AI fluency. Familiarity with AI-powered developer tools doesn't automatically translate to strong critical thinking about AI output or awareness of safety and privacy considerations.

What AI skills should founders prioritise?

Based on AISA data, founders' weakest dimension relative to engineers is technical understanding (49.1 vs 51.1). Investing in understanding how models work — context windows, token economics, model selection criteria — helps founders make better decisions about AI architecture and vendor selection. Founders should also maintain their existing strengths in critical thinking and safety, which are the two dimensions where they outperform engineers and the broader AISA population.

How reliable is this founder vs engineer comparison?

The comparison draws on 225 founder assessments and 490 engineering assessments — both well above the 30-assessment minimum for stable group-level statistics. AISA's test-retest data shows 98% of retakers land in the same or adjacent tier, and scores correlate at r = 0.79 between attempts, indicating the underlying measurement is stable enough for group comparisons at these sample sizes.