The AI Skills Leaderboard
Public ranking of professionals who chose to prove their AI skills — scored 0–100 on real fluency, not self-reported badges.
Where does your AI fluency rank against real professionals?
The AISA leaderboard ranks AI fluency scores from a 20–40 minute conversational AI skills assessment. Two AIs run in parallel — one holds the conversation, a second evaluates every answer in real time — across 11 criteria and 5 dimensions. Every score is tied to a direct quote from the candidate's own words. Top AI proficiency scores cluster between 80 and 90 out of 100.
Top AI skills scores
Opt-in rankings. Candidates can appear or opt out — they cannot edit their own scores.
How to rank on the AI skills leaderboard
Three steps. No exam. No self-reporting. The leaderboard earns its rankings the same way candidates earn their scores.
Start the conversation
Free 20–40 minute chat with Aisa, AISA's conversational facilitator. No quizzes, no multiple choice — just a dialogue that adapts to your answers.
Get scored in real time
11 criteria across 5 dimensions. Every score tied to a direct quote from what you said. You receive a detailed evidence-based report.
Choose to appear
After your report, opt in to publish your score to this leaderboard. You can opt out anytime from your results page.
What the scores measure
A rank here means one thing: the person ranked proved it across 11 criteria in a real conversation. Scores cluster between 40 and 90. Above 75 is strong working fluency. Above 85 is advanced practitioner territory.
- P1 — Prompt Design
- P2 — Iterative Dialogue
- P3 — Context & Memory Management
- T1 — Output Evaluation
- T2 — Limitation Awareness
- U1 — AI Fundamentals
- U2 — Tool Landscape
- W1 — Workflow Integration
- W2 — Task Decomposition
- W3 — Domain Application
- S1 — AI Safety & Responsibility
Full methodology: AISA Methodology · Deep dive into the rubric.
“Twenty minutes and I had a clearer picture of my AI skills than a year of self-study gave me. The growth plan was specific enough to act on immediately.”
Priya K.
Data Scientist
The 10 AI personas
Every candidate gets a persona — a profile of how they think with AI, not just how well they scored. Two people with the same 78 can receive different personas if their strengths fall in different places.
AI is on the radar, but not in the routine.
Has heard of AI tools but hasn't meaningfully engaged — the assessment itself may be the most direct interaction to date. Awareness exists; habit does not.
Tries things out — hasn't locked in a rhythm yet.
Experiments with AI intermittently: a prompt here, a quick question there. Nothing sustained, but a willingness to explore that many skip entirely.
Uses AI regularly — takes the output at face value.
Relies on AI for day-to-day output but with limited iteration or verification. Gets value, but leaves quality and safety gains on the table by accepting first-pass results.
Questions everything — the output, the tool, the hype.
Approaches AI with critical caution. May under-use AI in practice, but the verification instinct and risk awareness form a strong foundation that many frequent users lack.
Curious, capable, and picking up speed.
Actively building AI skill across multiple dimensions. Tries new tools, refines prompts, and is beginning to develop repeatable patterns — the trajectory is strong.
Gets things done with AI — fast and reliably.
Productive with mainstream AI tools and uses them well within established workflows. Communicates clearly with AI and consistently gets quality output, but typically hasn't pushed into the cutting edge of AI tooling or complex integrations.
Orchestrates AI across the workflow, not just within it.
Uses AI heavily across complex workflows, automations, and multi-tool pipelines. Understands AI limitations well and knows which tool integrates with which. Orchestrates and configures sophisticated setups, but typically works with what's available rather than building novel tools from scratch.
Has actually built something with AI.
Personally created complex, useful tools, workflows, or products using AI — whether for their own use, their company, or commercially. Developed deep practical understanding through hands-on building that goes beyond secondhand knowledge.
Builds highly complex integrated systems using AI.
Designs and builds sophisticated multi-system AI integrations at scale. Goes beyond creating individual tools to engineering production-grade architectures where AI components interact with each other and non-AI systems.
Understands AI at its core — not just how to use it.
Deep technical mastery of AI itself. Understands or builds AI models, works with ML and LLMs at a technical level. Elite critical analysis comes from understanding the technology at its foundation, not just from using it.
Read the full framework: The 10 AI Persona Types.
Why this ranking is different
Most AI leaderboards rank the models. This one ranks the humans. It measures how well professionals actually work with AI — not how clever the AI itself is.
Humans, not models
Scale, Vellum, and Artificial Analysis rank LLMs against benchmarks. AISA ranks the people who use them. The question isn't which model scored highest — it's who got the most out of the model.
Evidence, not self-reporting
Not a self-assessment. Not a badge you buy. Every rank is tied to direct quotes from a live conversation. Every score shows its receipt.
Conversation, not multiple choice
No quizzes. No trick questions. Candidates demonstrate skill by working with AI, not by picking the right answer from four options a test-prep course already taught them.
Frequently asked questions
Questions we get most often from candidates and employers landing here.
›Who scores highest on AI skills assessments?
The AISA leaderboard publishes opt-in scores from a conversational AI skills assessment. Top scorers typically cluster between 80 and 90 out of 100 across roles including developers, product managers, designers, and data scientists.
›How is an AI skills score calculated?
Each AISA score reflects 11 criteria across 5 dimensions — Prompting, Critical Thinking, Technical Understanding, Workflow, and Safety. Two AIs run in parallel during a 20–40 minute conversation: one converses, the other scores every answer in real time. A third pass recalibrates the final composite after the session ends.
›What is a good AI skills score?
Scores on AISA cluster between 40 and 90 out of 100. A score above 75 indicates strong working fluency with AI. A score above 85 indicates advanced practitioner-level command across all five assessed dimensions.
›What are the 10 AI personas on the leaderboard?
AISA classifies each candidate into one of ten archetypes: Bystander, Dabbler, Copy-Paster, Sceptic, Enthusiast, Tactician, Conductor, Builder, Architect, and Oracle. Each describes a distinct working relationship with AI tools, not just a score band.
›Where can I see verified AI skills scores?
Verified, opt-in AI skills scores are published at aisa.to/leaderboard. Entries are sourced from completed conversational assessments rather than self-reported badges, and candidates cannot edit their own scores — only opt out of public display.
›How can I get on the AI skills leaderboard?
Take the AISA conversational AI skills assessment at aisa.to/assess. The session runs 20–40 minutes. After post-session calibration completes, your final composite score is published to the leaderboard unless you opt out from the results page.
The Science Behind AISA
In 2026, Anthropic published the AI Fluency Index — the largest empirical study of AI fluency to date, analysing nearly 10,000 conversations. AISA covers 93% of the behaviours Anthropic identified as markers of AI fluency and goes even deeper with 4 additional dimensions. The U.S. Department of Labor's AI Literacy Framework (TEN 07-25) defines what every worker needs to know about AI — AISA covers 100% of its 25 sub-competencies.Read our analysis: Anthropic's AI Fluency Study & AISA · DOL AI Literacy Framework & AISA
AISA's framework is developed by a team with deep roots in tech, behavioural science, and AI product leadership — the rubric is informed by backgrounds spanning the Metropolitan Police, Harvard, Crowdbotics (Silicon Valley), and the European School of Economics.
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