I just became CEO of one of education’s Big 3. Here’s why AI will never replace a great teacher

McGraw Hill’s new CEO publicly rejects AI-driven doomsday scenarios and argues teachers remain indispensable while AI should be used to build teacher-centric tools. The piece cites a Harvard/Google study mapping 1 cubic millimeter of brain tissue (57,000 cells, 150 million synapses, ~705 million bits) to underscore human learning complexity and claims only ~1% of unicorns sustain long-term success. McGraw Hill is positioning AI as an augmenting technology—personalized content, adaptive labs and tools for educators—rather than student-facing answer engines.

Analysis

The piece crystallizes a structural bifurcation in edtech: infrastructure and scale players will continue to supply compute and generic LLM interfaces, but the durable commercial value will accrue to firms that embed workflows around human educators. Expect McGraw Hill (MH) and similar content incumbents to see an acceleration of monetizable SaaS adjacencies—adaptive assessment, teacher dashboards, and PD marketplaces—that convert one-off textbook revenue into recurring, higher-margin streams over 12–36 months.

Second-order dynamics matter: district procurement cadences, certification requirements, and union buy-in create multi-year adoption lags that favor companies with existing institutional contracts and salesforce penetration. That raises barriers for venture incumbents selling student-facing trophy apps: they can scale users quickly but struggle to translate usage into district budgets. Meanwhile big tech (MSFT, GOOGL) will win cloud, analytics, and LTI integrations, but face sticky trust, privacy, and procurement frictions that limit capture of curriculum dollars in the near term.

The real optionality is in “teacher augmentation” modules that reduce admin time and improve measured outcomes; these products can expand TAM by unlocking per-student spending currently trapped in legacy workflows. Regulatory and assessment changes (state test rewrites, federal grants) are the primary near-term catalysts—pushes that can reallocate budgets within 6–18 months and create discrete re-rating events for incumbents that execute.

Key Decisions for Investors

• Long MH (12–24 months): buy equity to play conversion to SaaS + assessment adjacencies. Base case +35% if 15–20% of legacy content revenue converts to recurring ARR; downside is procurement delays—use 20% position sizing with a 20% stop-loss.
• Pair trade (9–18 months): long MH / short AMZN — long MH to capture educator-centric monetization, short AMZN to express skepticism that consumer/cloud scale translates to curriculum budget share. Target asymmetric 3:1 upside:downside if MH captures district pilots and AMZN fails to land enterprise content deals; close if M&A announced between parties.
• Overweight MSFT (6–12 months) via call spread on institutional-focused strikes: play Teams/Classroom ecosystem revenue and Azure ed-cloud consumption. Expect steady moderation in volatility; hedge with partial profit-taking on any >25% run as enterprise spend normalizes.
• Tactical hedge: buy 12–18 month puts on speculative consumer/tutoring names (or small-cap edtech ETFs) to protect against a rapid rotation back into scale-first players if standardized testing policy incentivizes low-cost automated solutions. Aim for 2:1 payoff vs premium risk.