Wilson Wang

The through-line

Everything Wilson Wang argues comes back to a single conviction: education is the mechanism by which opportunity is equalised, and technology's job is to remove the frictions that keep it locked up. He frames the mission as bringing "the blockchain technologies AI technologies and the chat about technologies" into the education space "to really advance our vision of promoting a global education inclusion thereby enabling equality of opportunities for everyone in the world" [1]. The lineage he claims for that idea is national rather than technological: "I'm a Singaporean our founding father mr. Lee Kuan Yew right has let the whole the whole pioneer generation from that were to first because he believes the education embrace brings about the Equality of opportunities even though we cannot guarantee the equality of outcomes," and his argument is that AI and chatbots now make it possible "to bring their vision to the rest of the world" [1].

The second half of the through-line is a hard practicality about adoption. He is repeatedly impatient with projects that assume infrastructure will attract users, and insists on designing for the day after launch: "we are unlike other icos right we actually think about the day after the ico ok how do students want to why would they want to put information on this ledger" [1]. That question, who has an urgent problem today, dictates every design decision he describes.

On putting student records on a ledger

The concrete problem he starts from is paperwork. Applying to college after matriculation examinations means printing and scanning birth certificates, academic transcripts, essays and testimonials [1]. His alternative is a student-held record: "imagine a day right when a student just have to provide this hash key to the college they did he wanted to apply to and the college just unlocked this records right with the token that he can obtain from the crypto exchange," replacing the need for a university "to go through physical copies" and to store them [1]. He argues the model is most valuable precisely where documentation is weakest, since a digital ledger offers multiple routes to validate records, and "there is certain countries the student accessibility to such records is just not there and now that would truly an Eber the equality of opportunities for those students" [1].

He layers a second kind of record on top. Through a connection at the University of Cambridge, the platform embeds "apply magic sauce" personality profiling, letting a student log in with Twitter or Facebook to generate a Big Five personality profile that both sharpens university recommendations and is itself stored on the ledger, which he casts as "a win-win proposition for the students and the university" [1].

On not fighting bureaucracies

Wang is explicit that he will not try to force institutional adoption. Universities can keep their existing admissions offices; the ledger is offered as an alternative channel, an invitation to "try this out if you realize you actually get a more comprehensive way of unlocking students and proving epic applications," supported by a network incentive and a partnership pool in the token [1]. He puts the strategy plainly: "it's not either/or thing," and "instead of targeting the bureaucracies right or force them to adopt I target the immediate pinpoint of the students their confusion or what to do after high school and they are neat for affordable digital" [1]. The reasoning is sequencing rather than ideology: he does not believe a university would participate in a ledger that has not yet been populated, so students must have their own reason to populate it first [1].

On AI as counsellor and tutor

The immediate student pain point he targets is what happens after exams, when most school leavers "get funneled into the local universities and local colleges or the local tertiary institutions." He rejects that as a ceiling: "we don't believe that a child or teenager should be just limited to those options" [1]. His answer is an adaptive recommendation engine of the kind "typically found in the the YouTube Netflix and Spotify," repointed from videos and music to student records, so that accurate records produce global university options rather than "losing hope and losing losing their faith because they did badly in the metrication" [1].

Because a recommendation is a one-off event and he needs sustained engagement to learn user preferences, he extends the same engine into teaching. It learns the national curriculum of a given jurisdiction, starting with GCSE, O and A level sciences and mathematics, and delivers tuition through a chatbot interface, which he treats as a front end rather than the substance: "the Chabot is just a user interface" [1]. The economic case is the sharp part. Private tuition in Singapore and the UK runs "about 150 dollars per subject per month," which he calls "freaking expensive," against a $10 monthly subscription, or $7 for users paying in tokens, with the savings on payment processing passed through to the user [1]. The intent is to give ordinary families the tuition advantage "that's the privileged families will usually have" [1].

On open-loop versus closed-loop token design

He draws his sharpest competitive distinction here, and does it without disparaging others: "casera is a phenomenal online academy but an online Academy if you do an icy old from there the tokens can be used only within deaths that Academy it's just like a Starbucks card you can only grow within Starbucks" [1]. His own design is open-loop because the AI learns whatever curriculum a jurisdiction runs, which makes geographic expansion a matter of ingesting a new syllabus rather than building a new product. The stated sequence is the UK, then India with its CBSE and ICSE curricula, then Indonesia and Central Asia, with the ambition to cover "every single curriculum required for any country" [1]. He wants that rollout fast, describing the intent to "uber ize our global expansion" [1].

On why the UK first

Singapore is the base but "gets too small," so the first target market is the UK, chosen because Singapore is a Commonwealth country whose O and A level system is modelled on the British one, making the curriculum familiar and the learning curve short [1]. His argument is that once the machinery of learning one national curriculum is built, "expanding out right becomes a lot easier" [1].

On the investment case and token economics

Asked bluntly how investors make money, he does not deflect. Education is "four point eight trillion dollars in evergreen markets," and his central claim is a collision of two forces: "for the first time in history right the inflationary cost of education ... is now [offset] by the deflationary nature of cryptocurrency" [1]. He glosses the terms carefully, deflation as an increase in purchasing power and inflation as a decrease, and projects that a token worth a dollar today could be worth considerably more later [1].

He is pressed on the obvious objection, that an appreciating token makes tuition unaffordable, and answers that services are priced in fiat, so the token rising in value does not raise the price of a lesson: pricing tracks the general economy, perhaps two percent, "not an s-orbital one," because the whole point is a cheap alternative to private tuition [1]. He extends the appreciation argument into a contribution loop, suggesting students who write good revision notes should contribute them to the community and be rewarded in tokens that "20 years down the route can support the education of your children" [1]. On liquidity and venue, he describes taking what he calls the more onerous regulatory route to a government-supported stock exchange requiring a pre-approved sponsor, while also securing listings on exchanges he ranks around seventh and eighth for liquidity, on the reasoning that "for my early supporters and investors I know that liquidity is very important" [1]. He also reports interest from a market-data listing site on the strength of the education use case [1].

On disintermediating education philanthropy

The application he seems most excited by is giving. Today a donor "have to go through intermediaries governments where potentially for these cents on the dollar and so in the pockets of corrupt officials or in efficiencies of bureaucracies" [1]. With a global ledger of high school students, a donor can buy tokens on an independent exchange and transfer them directly into a student's digital wallet for digital tuition ahead of matriculation exams, with access to the academic records that then justify further sponsorship [1]. He sizes the opportunity at roughly sixty billion dollars of education-related philanthropy a year, where capturing even one percent would be six hundred million, and argues this "opens the whole world of new opportunities for everyone" [1].

Takeaways

• Design for the day after launch: the test of an education network is why a student would put their records on it, not whether the infrastructure exists [1].
• Do not ask bureaucracies to adopt anything. Offer the ledger as an optional additional channel to universities while solving the student's post-exam confusion directly [1].
• Recommendation engines built for YouTube, Netflix and Spotify can be repointed at student records to surface global university options for teenagers otherwise funnelled into local institutions [1].
• Price against the real alternative: private tuition at roughly $150 per subject per month versus a $10 subscription, or $7 when paid in tokens with payment-processing savings passed on [1].
• Closed-loop education tokens are "just like a Starbucks card"; an engine that learns any national curriculum makes expansion into the UK, India, Indonesia and Central Asia a matter of ingesting a new syllabus [1].
• Keep services priced in fiat so token appreciation benefits holders without inflating the cost of a lesson [1].
• Direct token transfers to student wallets remove the intermediaries that skim education philanthropy, a market he sizes at about sixty billion dollars a year [1].