Peter Yang interviews Sue Khim, co-founder and CEO of Brilliant (the interactive learning app) — a working-parent conversation about why AI is a cheating machine today and how tutoring software can make kids do the hard thinking themselves. ~46 minutes on Peter Yang’s channel.

The US is in a learning trough

  • Test scores are down but grades are up — schools quietly lowered standards (grade inflation) to appease parents, and the delta only shows up on internationally graded exams
  • AP English drifted from undergrad-level passages to middle-school reading in roughly two years; colleges are now teaching remedial middle-school algebra
  • The upside: the US still has room for experimentation — new home schools and experimental schools everywhere, which she says are the conditions new solutions come from

AI: cheating machine or sea change?

  • “AI today is mostly used by students for cheating” — offloading the thinking is like bringing a robotic arm to the gym to lift weights for you
  • Used right, she thinks AI can make kids into geniuses — “a sea change like the invention of writing suddenly allowed everyone to have infinite memory”

Brilliant’s AI tutor Cooji: no answers, and no explanations either

  • Demo of a real tutoring session: a student stuck on fractions; Cooji walks them back through the mistake with questions instead of revealing the insight
  • Her claim: explanations and cheating are closer than you’d think — both let the learner off the hook, and an explanation only makes sense if you already understand
  • The product deliberately keeps learners in the struggle (“why is this wrong?”) until they find the insight themselves — worse for short-term engagement, but she calls engagement-maxing answers a “local maximum” that loses on learning

Software beats classrooms — and private tutors — at instruction

  • Classroom time is best spent on peer learning and teacher motivation (“enthusiasm is infectious”), not lectures
  • One-on-one software tutoring wins on cost and convenience, real-time graphical interaction, adapting on the fly, and no fear of embarrassment — most kids will never raise their hand

Building with agentic workflows: AI in the middle, not end-to-end

  • Models are “very spiky”: great at implementation, terrible at designing a pedagogical sequence — and the tasks they’re bad at ruin the whole experience
  • Brilliant tried training Claude/Codex on its entire corpus to generate lessons end-to-end: output “looks almost right” but is nowhere near master-teacher quality — kids can tell, and engagement metrics prove it
  • Every lesson’s core pedagogy is designed by a human, then implemented by AI — “companies that are selling an experience driven end to end by an LLM are going to disappoint their customers”

The agent interface is as important as the user interface

  • A library of modular “primitives” (graphing, problem creation, annotations, sub-questions…), each with an API a model can write to — deterministic grounding so the model isn’t hallucinating what to do, with better latency and accuracy
  • “Everyone’s job is now to define an agent interface for their area” — AI as the connective tissue of the company, with onboarding built around shipping something user-facing on day one

Quality: evals, human reviewers, synthetic students

  • Evals on every primitive go beyond correctness: obeying the laws of physics, no overlapping UI elements, generous tap targets
  • Synthetic students run ~1,000 tutoring sessions per lesson down adversarial and unhappy paths — “20% as good as real human data, but better than nothing”
  • They don’t measure completion rate or time spent — only performance on the assessment: can you do the thing today, in a week, in a month?

Why tutoring is defensible against frontier labs

  • “If the big model companies can RL your domain, they will eat you alive” — anything automatically verifiable, like coding, belongs to them
  • Tutoring isn’t: the outcome is a real human who actually learned and sustained attention, so you need real tutoring sessions at scale — and that data is the flywheel that makes the tutor better
  • Frontier labs’ homework-help logs are useless for this — they never know whether the student learned

The homework incentives problem

  • The US is unusual in grading homework; other countries grade the test and treat homework as reps — where homework counts toward the grade, kids rationally take the shortest path, and AI is it
  • Fix the incentive, not the students: don’t grade homework, and make assignments project-based — grade what they make, not whether they filled the sheet in correctly

“If the big model companies can RL your domain, they will eat you alive… there’s lots of alpha finding domains that no one else can RL on. And tutoring is one of those.” — Sue Khim