Why AI Built for Everyone Fails Schools

Since starting an AI-native education technology company a few months ago, one of the questions I'm asked most often (usually by people coming from the broader technology or business world) is whether I worry about larger, well-capitalized AI companies like OpenAI or Anthropic eventually copying what we're building. My answer is no—not because I underestimate their technical capacity, but because the incentives that govern these companies are structurally misaligned with what schools actually need.

The dominant AI companies are rewarded for producing systems that are frictionless, broadly useful across domains, and immediately satisfying to the largest possible audience. Education, by contrast, depends on tools that deliberately slow students down, preserve productive struggle, and make thinking visible rather than invisible. In addition, companies building for the classroom bear a heightened responsibility to protect the data of minors and to reinforce sustained adult oversight.

In the context of schooling, these qualities are not optional features or ethical add-ons; they are the substance of the work. For companies whose business depends on serving everyone at once, however, such constraints can only ever appear as a special mode, an edge case, or—more often—a liability that cuts against the very qualities that make their products valuable in the broader market.

This misalignment is the real reason Socra can't be easily copied. It's not that OpenAI or Anthropic lack the technical ability to build a classroom-specific interface. In principle, they could build something that mimics Socra's surface features, such as teacher dashboards, writing workflows, and “show your work” prompts. But Socra isn't a bundle of features; it's a set of commitments—pedagogical, operational, and ethical—embedded in the product's defaults. And those defaults are costly for general-purpose AI companies, because they require tradeoffs against the very experience those companies are optimized to deliver.

General-purpose models are designed for efficiency and convenience. They are built to produce a good answer quickly, confidently, and with minimal user effort. Ultimately, these companies attract users by promising to save time, reduce cognitive load, and replace tedious tasks. “Maximally helpful” in consumer AI means the model tries to complete the user's intent as directly as possible. But in education, “maximally helpful” is often antithetical to good pedagogy. The whole point of a learning environment is that the student must do the thinking. A tool that defaults to answer-giving undermines the very outcomes teachers are trying to produce, such as independent reasoning, the ability to revise through uncertainty, and (perhaps most important) the development of an authentic voice.

So if a large AI company attempted to build a “Socra,” it would face an immediate contradiction. To be safe and useful in schools, the tool must be intentionally less convenient; it must expose process rather than hide it, refusing some requests, redirecting others, and logging interactions in ways that support teacher oversight. Those choices would feel like “worse product” by the standards of the broader market (and that broader market—not classrooms—is where these companies live or die).

Consider what teachers will actually need from AI in 2026. They will not need better answers or more polished writing, but visibility into how answers were formed or how writing came into being. They need tools that can translate student interactions into legible evidence of thinking. In addition, teachers will need systems that respect classroom norms about when help is appropriate, what kind of assistance is permitted, and at what stage of the learning process intervention should occur. They need reporting that supports judgment and feedback rather than suspicion and enforcement. And schools need the assurance that student data will remain bounded by its educational purpose, rather than quietly absorbed into a general-purpose asset pool.

General-purpose AI companies struggle to meet these needs not because they lack intelligence or resources, but because doing so would require them to work directly against two of their strongest incentives: scale and generality. At scale, systems cannot afford to bend to the lived reality of individual classrooms without becoming unwieldy, so they standardize; and in the pursuit of generality, they are rewarded for abstracting away the messy particulars. What education demands, however, is precisely an attentiveness to those particulars, not their erasure.

Complicating matters further is the fact that schools are not a single market but thousands of distinct contexts, each shaped by their own institutional constraints, community norms, and pedagogical commitments. Serving this landscape well requires more than a menu of configurable features; it requires an organizational posture oriented toward sustained, day-to-day listening, and a willingness to make design choices that render a system less seamless and less magical in exchange for making it more accountable to the people who use it.

Socra's advantage, then, is not primarily technical but structural. Because it is built exclusively for education, it can treat teachers as the primary stakeholder rather than an edge case, setting defaults that preserve learning rather than optimizing for speed, fluency, or completion. It can design for transparency as a first principle, ensuring that students' interactions with AI remain legible, reviewable, and discussable, rather than dissolving into an invisible process that leaves teachers guessing. It can support students without quietly transforming their work into an AI-generated artifact; and it can treat responsible use not as a marketing slogan layered onto the product after the fact, but as a core design constraint that shapes the system from the inside out.

That Socra is built by teachers matters here in a concrete, non-symbolic way. Teachers are not asking for a safer chatbot; they are asking for tools that fit into the rhythms and realities of classrooms as they actually exist. They want language that mirrors how they give feedback, interfaces that respect their professional judgment, and systems that allow them to decide when help is appropriate while retaining visibility into what a student actually did. When you are building from within education, the default assumption is no longer that the model should finish the task efficiently, but that it should support the student in doing the task themselves.

Socra's daily feedback loop is similarly difficult to replicate at scale. Large AI companies can and do conduct user studies, but their signal is necessarily diluted across millions of use cases and competing priorities. Socra's signal, by contrast, is concentrated: teachers and students, every day, in real classroom conditions. That focus changes not only what you notice, but what you choose to prioritize. Instead of chasing impressive demos, you begin building the unglamorous infrastructure—controls, logs, rubrics, and review tools—that make sustained adoption in schools possible. In this context, those features are not ancillary; they are the product.

The question, then, is not whether general-purpose AI companies could enter education, but whether the market incentives that make them successful elsewhere can ever align with what schooling is actually for. Education rewards friction, accountability, and patience; the AI market rewards speed, scale, and convenience. Until those incentives change, the future of educational AI will not be shaped by companies optimizing for everyone, but by those willing to constrain themselves to building tools that privilege visible thinking over convenience, accountability over scale, and learning over speed.