At Trilogy in the 1990s, Joe Liemandt built and acquired software companies, trained thousands of elite recruits through “Trilogy University,” and obsessed over operational playbooks. That same playbook shows up in his schools: design the whole stack, measure relentlessly, and invest where it moves outcomes. He’s pledged $1B toward full-stack reinvention—campuses, staff, software, and culture—arguing that edtech alone can’t fix a broken system.
“If we’re going to put kids in school for 12 years, they should love it so much they’d choose it over vacation.”
Kids must love school.
Alpha measures this explicitly; Liemandt says ~96% of students report loving school, and a striking share would rather attend than go on vacation. Love isn’t fluff—it’s a leading indicator of engagement and persistence.
Crush academics in two hours.
The AI tutor delivers personalized, right-level lessons that adapt by concept, not by grade label. In Liemandt’s framing:
Personalization fixes the “wrong level” problem (e.g., sixth graders who still need phonics).
Acceleration helps both gifted and behind learners; Alpha cites cases moving students from bottom 25% to top 25% in ~two years.
Outcome claim: Over 2× learning compared to six hours of class + homework.
Life skills power the rest of the day.
With core academics condensed, students spend four hours in workshops on leadership, teamwork, public speaking, entrepreneurship & finance, relationship building, and grit. Think project-based, adult-coached sessions that feel closer to a startup studio than a lecture hall.
Guides and coaches > front-of-room lecturers.
Adults in the building focus on motivation, mentorship, and emotional support while the AI tutor handles direct instruction. Ratios and staffing adjust by age (more structure in K–2, more autonomy in middle/high).
Character, community, culture.
Schools aim to graduate good people—not just high scorers. One operational metric for critical thinking: steel-man both sides of a debate before claiming mastery.
Liemandt is emphatic: “Motivation is 90% of the solution.” The model treats motivation like a product with layered levers:
Time back (the #1 driver): Two hours of academics buys students hours of meaningful work they actually want to do. When school unlocks autonomy and purpose, compliance turns into ownership.
Individualized triggers: From sticker charts in early grades to Swift dance-offs for middle schoolers—the small, specific reinforcers that make the next lesson feel attainable.
Earning & incentives (controversial by design):
Clear targets like top-1% performance bonuses for middle schoolers.
“Learn & Earn” stipends in refugee programs and public-school pilots to jump-start daily habits.
Confidence unlocks: Short-term rewards to prove “I can,” then fading the incentive as identity takes over.
Screen time swaps: Trade AI-tutor time for approved gaming/reading time—a practical nudge most families can live with.
“We’ll pay to start the habit—but the goal is identity: I can do hard things.”
Alpha results: Claims of top-1% academic performance across cohorts; gifted tracks “off the charts.”
MTSS Tier-3 pilots: When daily effort is motivated explicitly (e.g., small gift cards + streaks), students who were far behind engage and progress.
Ukrainian refugee program: A student-designed “Learn & Earn” model (≈$2.50/day + streak bonuses) kept learners consistently on task; Alpha reports learning rates comparable to their private model despite scarce resources.
(Note: These are program-reported outcomes from the conversation. Independent third-party evaluations would be a valuable next step as the model scales.)
AI isn’t optional. Deployed poorly (e.g., generic chatbots), students cheat; deployed well (structured tutors), students thrive. Liemandt argues bans harm kids by removing the era’s most powerful learning tool.
Teach principles, not just tests. The schools articulate American civic values while teaching critical thinking via two-sided debate and evidence.
School choice as a scaling mechanism: Different families want different things. Let multiple models compete and replicate what works.
Whole-school redesign: Not “add an app,” but rebuild the day around a two-hour AI core, then invest in life skills and culture.
Operator mindset: Aggressive measurement, staffing to the job to be done, and willingness to use every ethical lever of motivation.
Ambition & capital: A stated goal to reach a billion students, driving costs down from U.S. private-school levels to sub-$1,000 on-device delivery over time.
If you’re wrestling with time, engagement, and uneven readiness, the 2 Hour Learning model offers a blueprint: personalize academics, free time for meaningful work, professionalize motivation.
If you’re exploring AI adoption but stuck in policy headwinds, this is a clear, bounded use case (structured tutoring with supervision) that avoids the “cheatbot” trap.
If you serve Tier-3 or behind learners, treat motivation as infrastructure—design it as deliberately as your curriculum.