A decade ago, a wave of adaptive learning software arrived with claims that look a lot like Alpha’s. Programs that met students at their level, advanced them through content at their own pace, and personalized practice were going to transform achievement. Districts bought in, the transformation mostly did not arrive, and the backlash that followed hardened into a skepticism that still colors how many educators hear any new technology claim.
We think there is a more precise and useful way to make sense of the failure. Illustrative of the lack of results is what Laurence Holt, a Senior Advisor at XQ Institute and author of The Science of Tutoring, has documented: a pattern he calls the five percent problem. Studies of certain edtech programs find that they can produce real learning gains, but only when students use them as intended. The kicker? In these studies of edtech usage in schools, only about 5% of the students ever reach that threshold of faithful use. As a result, when a study finds weak average effects across schools that bought a program with evidence of producing learning gains, it’s not really measuring whether the technology can work if used. It measures how few students actually used it at the recommended dosage.
There are of course many other reasons why edtech has failed—faulty incentives and procurement challenges that result in edtech with lousy instructional design getting adopted; a focus on pacing guides over mastery; the distractions that result from introducing edtech; and more. But they all center around the challenge outlined in Holt’s research. The failure of the last edtech wave was not a failure of software per se. It was a failure to redesign the surrounding system of the schools themselves to effectively take advantage of the technology.
An analogy from aviation captures why redesign matters. When jet engines arrived, simply replacing a propeller engine with a jet engine did not automatically transform the capabilities of an airplane. The new technology created possibilities that conventional airframes were not designed to exploit. Realizing those possibilities required changes to the aircraft itself: pressurized cabins, stronger fuselages, swept wings, and other features suited to higher speeds and altitudes. The engine was the prominent innovation, but much of its value came from redesigning the aircraft in conjunction with what the new technology made possible.
Likewise, the last wave of schools bolted new instructional technologies onto an educational model built for a different era and then wondered why student learning didn’t take off. They adopted the engine without substantially redesigning the airframe along with it.
Schools treated the software as something you could plug into the existing architecture of a classroom the way you would add a new app to a phone. Drop the program in, assign students to it a few times a week, and expect results. But the technology and the classroom around it were interdependent. The software’s effectiveness depended on how class time was structured; on what the teacher did while students were on the computer; on how student effort was motivated; on whether the culture of the room treated the program as central or as a babysitter for the back half of the period; on curriculum pacing, assessments, and grading; and so forth. Plugging a genuinely interdependent technology into an architecture that was never redesigned to receive it is a recipe for exactly the mediocre results the last decade produced. That was the logic behind our work over a decade ago detailing blended-learning models: that the model itself had to change for technology to be of any use in bolstering student outcomes.
What Alpha built instead
A second aircraft example parallels Alpha’s strategy. When Lockheed’s Skunk Works built the SR-71 Blackbird, the fastest air-breathing aircraft ever flown, they faced a budget problem on top of an engineering one. The plane was already extraordinarily expensive. Designing a bespoke engine from scratch would have made it more expensive still. So they didn’t. They took existing Pratt and Whitney engines, technology that was already good enough at the raw job of producing thrust, and they poured their genius into everything around the engine, most famously the movable inlet cones that tamed airflow at three times the speed of sound. The integration, in other words, was the innovation, not the engines. The choice to buy rather than build it was as much about up-front cost as about engineering.
Alpha seems to have followed a similar playbook. Its two-hour learning block originally contained the equivalent of off-the-shelf engines. The underlying instructional software products weren’t, for the most part, proprietary AI-powered breakthroughs in how content gets taught. They were existing adaptive-learning tools, the same general class of software that the last edtech wave deployed to such underwhelming effects. Custom-building the underlying learning software from the ground up would have been expensive and slow. Fortunately, the existing off-the-shelf software allowed Alpha to make the SR-71’s choice: take software that is already good enough at the raw job of delivering instruction and practice and spend money instead on the custom layer wrapped around it, the part that makes off-the-shelf engines perform like something far more advanced.
Alpha, however, went farther than most schools by redesigning the “airframe”—the rest of the systems around the software. The school rebuilt the role of the adult by stripping out teacher-led instruction and pouring its guide’s entire energy into motivation and coaching. It rebuilt the structure of motivation, with the bargain of students getting their time back for fun activities in the afternoon if they focus and do their morning work. It also created a currency system that we described in our earlier piece for rewarding students for buckling down and making progress on software-based learning. It rebuilt the culture of effort, so that focused work on the software became the organizing expectation of the morning rather than one activity among many. It built a proprietary AI-powered platform, Timeback, that sits on top of adaptive learning applications to monitor students during their two-hour learning blocks and provide feedback to both students and guides on students’ engagement and efficiency. These choices are a coherent answer to the exact problem that sank the last edtech wave: Alpha redesigned the “airframe” to fit the “engine.”
Alpha’s new challenge
For the families Alpha serves currently, based on parent testimonials and the school’s self-reported outcomes, it seems that arrangement has, on balance, worked.
But according to Alpha’s leaders and a variety of other reports, Alpha is increasingly building its own digital curricular products. Why might it be spending lots of time and money to do so?
Our theory-informed view is that as Alpha has sought to expand—both in the footprint of its own schools and through offering Timeback to other schools—it’s likely that its original software and curricular architecture is hitting a wall in just how much better it can get. And it must get better, as it seeks to serve students who likely have more-demanding needs than its original set and schools outside of the direct control of Alpha itself. In the language of Disruptive Innovation, Timeback must go up-market to take on more complicated problems.
Why might Alpha’s software and curricular architecture have hit a wall in terms of just how much it can improve? Our suspicion is that it was the lingering unreliable interdependencies between the off-the-shelf software and Timeback.
The outputs of third-party learning software are noisy. Those products were built to deliver their own instruction and report on their own terms, not to hand Timeback a granular picture of what students know and where their misconceptions lie. Alpha can see that a student finished a module in an outside program. It has a much harder time seeing why the student struggled, or what to serve up next.
The assessment layer compounds the problem. Alpha didn’t build its own assessments. It uses off-the-shelf instruments and pulls their results into Timeback to help build each student’s plan, which is more than most edtech platforms have ever managed. But an assessment instrument administered a few times a year is a coarse tool for a system that wants to make daily decisions. It tells you where a student ended up. It says very little about what happened in between.
And as Alpha seeks to serve students in more-demanding circumstances, that visibility and feedback into student performance is something to improve upon. Another theory from Christensen’s work—Modularity Theory—helps us understand why Alpha is gradually moving away from third-party edtech curricular products.
When an innovation is new, its developers often rely on existing products for other parts of the system that surround the innovation, because in the early stage you can’t afford to custom-build everything. Combining a new innovation with off-the-shelf components is fine when you’re serving a context where the alternative for your customers is either something at odds with what they really want or nothing at all. But as a company pushes into more demanding applications, it discovers that its product isn’t yet good enough for what those customers expect.
According to Modularity Theory, getting good enough means the parts of the system have to be designed together, tuned to each other, and optimized as a whole. The pieces become interdependent, and you can’t swap one out without rethinking the others. Only later, once performance is more than good enough, and the interfaces between components are well understood, can a system become modular again, with clean standards that let anyone mix and match parts from different suppliers without the whole thing falling apart. The dominant product architecture in an industry tends to shift back to a modular state once consumers’ needs are overserved and they are looking for more affordable customization.
The Ford Motor Company evolved in that sequence. Early in the Model T era, Ford bought steel and many components from established suppliers. But as it pushed to scale and streamline production, dependence on outside companies made it harder to control quality, costs, and the flow of materials. The inputs and final output were just too interdependent without clear and definable modular interfaces. Ford therefore brought more and more production in-house, which culminated in building the enormous River Rouge complex. Here Ford made its own steel, glass, castings, and many other components. Later—beginning around the transition from the Model T to newer models—the company moved to rely increasingly on independent suppliers. The reason? It needed to focus increasingly on scale and cost control, which outweighed the need to control the interaction of every single component in a car, which had become stable and good enough. With the basic car architecture stable and good enough, Ford could clearly define what it needed from outside providers and ensure that they met its standards. From the perspective of consumers, they were increasingly looking for customization. Any car so long as it was black—the early motto of the Model T—was falling out of favor.
Alpha is in the beginning stages of a similar progression—from off-the-shelf components to creating nearly all of its own digital curriculum in a highly interdependent architecture with Timeback to push the bounds of its software and serve students in more-demanding circumstances. To keep pushing the frontier of performance and reliability, Alpha has to own the interfaces it was previously borrowing. This is expensive, but if Alpha hopes to scale Timeback, it’s likely necessary.
The bet beneath the school
This context brings us back to the argument around whether Alpha is disruptive.
Let’s start with two of Alpha’s choices that many other educators and we find hard to defend on pedagogical grounds. Guides who know their students well are forbidden from stepping in to teach a concept when a child is stuck, and the morning’s academics are walled off so completely from the afternoon’s projects that neither informs the other. In our earlier companion piece, we treated these decisions as real tradeoffs, which they are. But understanding the above analysis helps us understand why Alpha made these tradeoffs.
These weren’t pedagogical decisions viewed through the eyes of serving one student or optimizing the transfer of skills, but those of an organization seeking to dramatically improve the pedagogy of its core technology. Both the choice not to allow a guide to instruct and not to connect core academics to the afternoon’s projects allow Alpha to create a cleaner feedback loop in the interface between the student and the digital curriculum.
If guides taught, Alpha could no longer tell whether a student’s gain came from Timeback or from the adult who intervened. If the afternoon’s messy, social, hard-to-measure learning bled into the morning’s metrics, the signal would further blur. Holding everything but the software constant helps understand what the software itself is actually doing—which allows Alpha to better improve it over time.
Building and tuning that custom layer is enormously expensive, and the conditions Alpha enjoys are favorable: families who likely reinforce learning (intentionally or not!) at home plus a wealthy CEO and expensive tuition to fund the engineering. The school, with its high price and its advantaged families, is perhaps a mechanism for paying to develop the technology under ideal conditions. The students are, in the language of disruptive innovation, relatively undemanding customers in terms of raw academic need, which is exactly the population against which you would want to refine a still-maturing product before exposing it to harder cases. Every cohort of high-paying families is, among other things, a round of funded research and development for the Timeback platform underneath.
This helps understand why the platform, and not the school, is where the most credible case for a disruptive strategy lies.
As we’ve discussed, Alpha has already been playing with offering its Timeback platform in other settings—as a standalone virtual platform and for other schools. If Timeback can be made to be “good enough,” then it could become a robust product offering for schools that adopt certain principles from Alpha’s model. And these schools could cost significantly less than Alpha’s $65,000 flagship. That is the move that would matter. A technology enabler—Timeback—that lets a low-cost school deliver strong mastery learning without having to independently solve the integration problem from scratch is the kind of thing that genuinely could climb the market the way real disruptions do.
With that said, we see at least two things that could keep Alpha’s potentially disruptive strategy for Timeback from working out.
The first is hype. Alpha’s leadership team has made expansive public claims, in the manner of a Silicon Valley launch, and the gap between a polished demonstration under ideal conditions and a product that works in the hands of strangers is where a great many education technologies have died. Selling the promise before the product is ready is its own kind of risk, especially in a field as allergic to overpromising as education has become.
The second doubt runs deeper. It concerns whether the integration Alpha has achieved can survive being modularized. Today Alpha controls everything: the students, the families, the guides, the use of time, the culture, and the conditions. That total control is what makes the clean feedback loop possible. But the whole disruptive promise depends on eventually pulling the platform out of that controlled environment and plugging it into other schools with other students, other cultures, and far less favorable conditions. The question is whether the integrated software that Alpha builds will be able to be a modular component within someone else’s school and can perform when transplanted—or whether its performance is inseparable from the more integrated schools in which it started.
To be clear, it’s possible and advisable that Alpha put some non-negotiables around the schools that adopt Timeback: mastery-based learning, a clear motivational model, and the like. But Alpha’s results may owe less to the platform than to two other things that travel poorly. The first is the pieces of the instructional model that live in a school culture, not in the technology. The second is admissions and the families themselves, whose social and educational capital quietly fills gaps the software never had to address. If Alpha’s success depends more on these factors than on a finely tuned and scalable software platform, then the technology sold to a school down-market could arrive without Alpha’s most important ingredients.
According to one early case study, the results on paper at least don’t look promising. Unbound Academy opened in Arizona in the fall of 2025 as a public charter school built on the two-hour learning model, with Alpha co-founder MacKenzie Price as its board president. Unbound is free. By law, it can’t screen applicants. In its charter application, it promised the state 60% proficiency in math against a baseline of 34%, and 65% in reading against a baseline of 40%. Its first year came in at 10% in math and 28% in reading, below the Arizona state averages in both subjects and below the baseline it had projected for its own students.
There are already some questions about these results—students may have entered even lower than what the school projected, for example. And just one year of results from one school is certainly not a final verdict, as Unbound could post better numbers as it finds its footing. First years are hard everywhere. But the distance between the projection and what Unbound delivered is wide enough to take seriously. They also fit within the precise set of concerns we’ve raised. It’s also, notably, the same conclusion that Alpha appeared to reach last spring when it pulled back on rolling out Timeback to many potential partner schools.
What we are actually watching
This story doesn’t have an ending yet. The fair reading of Alpha, through the lens of disruption, belongs to neither the booster nor the skeptic. The school itself is unlikely to be a Disruptive Innovation.
But the school may be a clever instrument for funding the development of a wave of education entrepreneurs that could be—and the school’s leadership could be bringing the requisite marketing attention to it. The design choices that read as pedagogical flaws through one lens look different when you see them as the disciplined behavior of a company refining a platform.
Alpha’s decision here is worth appreciating. The usual way to build educational technology is to sell into schools you don’t control and then try to improve the product from a distance, while every partner school runs a different model, staffs it differently, and holds a different theory of what the software is for. That arrangement produces exactly the muddy signal that doomed the last edtech wave.
Alpha instead built the schools, hired the adults, set the culture, and ran the platform under conditions it could hold steady. Owning the whole system is expensive, and it limits what a company can learn about contexts unlike its own. But it’s a far better way to find out whether a technology works than negotiating with two dozen partners who each want something different.
There is one more thing sitting in the decision to build its own school and software that cuts against the most common criticism of Alpha. The usual objection is that a school costing $65,000 a year serves only families whose children were going to be fine anyway, and that whatever Alpha proves cannot be claimed as a contribution to anyone else’s kids. That objection has real force when it comes to who benefits today. But it misses something about who is absorbing the risk. Education has a long and unhappy record of trying unproven models out on the students with the least ability to walk away, financed by public dollars that could have gone to something that already worked. When those experiments fail, the children who bore the cost had no say in the matter and little recourse afterward.
That is not what is happening here. The money financing this bet is Liemandt’s own, and the tuition sustaining it comes from families who looked at an unproven model, understood that it was unproven, and chose it anyway. If two-hour learning turns out to be less than advertised, the people who paid for that discovery are the ones who volunteered to fund it, and the rest of us will have learned something at no cost to our own students. To be clear, that calculus changes the moment a model like this moves into public education, which is exactly why Unbound deserves the scrutiny it is getting. But for the phase Alpha is in now, having a wealthy CEO and willing families underwrite the experiment is not a mark against the project. It is the most defensible way to run one.
Whether Timeback ever escapes the test range, performs in ordinary conditions, and reaches the students who would benefit most is the thing actually worth watching over the next several years. That is a far narrower and more answerable question than the noisy argument now raging about whether Alpha is the future of school or a mirage for the rich. It is also, we suspect, the question Alpha’s own leaders care about most, whatever their marketing says. The school is the part everyone is arguing about. The platform is the part that, for better or worse, might help shift the broader education landscape.
