The Trillion-Dollar Blind Spot

I am the founder of ForeMetric, an infrastructure protocol that turns behavioral signals from a smartphone into passive income for the user and verified predictive insights for the business buying them.

Project status as of today: pre-seed, preparing for the alpha test in December, fundraising round is open.

What we are building has only become technically possible now.
Several factors that did not exist five years ago have converged.
Modern smartphones carry hardware security elements — Secure Enclave in iPhone, TrustZone and StrongBox in Android — that physically isolate encryption keys from the operating system itself.

Open LLMs now fit in a gigabyte and run on the device's NPU, classifying behavior locally without sending raw data anywhere. Confidential computing on the server side — AMD SEV-SNP, Intel TDX — turned “we promise not to look” into “we physically cannot look”.

A cheap blockchain, one we did not build ourselves but took off the shelf (one of the fastest-growing networks around), let us bake revenue distribution into immutable contract code.

Each of these pieces existed in isolation. Assembling them into one circuit, where a person owns their behavioral capital and receives a share of its commercial use, has only become possible in this particular technological window.

The gray import of data

The behavioral data market today works like a gray-market import.
Apps and SDKs collect signals, brokers stitch them into profiles, ad platforms buy them in segments. And at no point in this chain does the person whose behavior became the product sign anything. In the US alone there are hundreds of such brokers; the top of the market moves billions.

And every year this arrangement costs more for the people who run it.
In 2024, an aggregator called National Public Data leaked roughly 2.9 billion records: names, addresses, social security numbers.
When Apple turned on App Tracking Transparency in 2021, Meta itself estimated its losses the following year at about ten billion dollars.

A single checkbox in a system dialog cost the company more than the annual budget of the European Space Agency. Collecting without consent has become a channel with a rising risk price: fines, leaks, operating-system-level blocks.
Meanwhile, the question the market still cannot answer is simple: where does a business buy ready-made answers about what users will do next?
Not the same datasets and logs their analytics departments decode and study on their own, but finished forecasts of near-future behavior.
The business pays for the forecast, the people whose behavior it describes take a share of every payment. No such channel exists.
That is the need our project is built for.

Basic income, but for a resource

The basic income debate rests on one sticking point. Supporters talk about a right to exist. Critics ask why anyone should receive money for nothing, and who pays.

Both sides quietly agree on one thing: the money will come from taxes, which means from someone else's work.

History has a third template. In 1797 Thomas Paine published a pamphlet called Agrarian Justice: pay every adult a one-time sum on reaching majority, funded by a tax on landowners. His argument was about property, not charity: land in its natural state is common wealth, and those who fenced it off owe the rest compensation for exclusion.

Ground a payment in a resource and it stops being a handout. Alaska has been running the working version since 1982: the state sells oil from under its land and every resident receives an annual dividend.

The first checks, for one thousand dollars each, went out on June 14, 1982.
Behavioral data is the commons of our era, only fenced off without a single signature. Every person with a phone produces valuable signal every day, and the entire supply chain is built to take it silently. ForeMetric's philosophy is simple to state and hard to build: put a price on the resource and pay the owners. Eighty percent of revenue from each forecast report is designed to go back to the people whose vectors made that forecast possible. “Universal” because everyone produces the signal. “Basic” because it is the floor, not the ceiling. “Income” because we pay for the resource.

The hard part

The hard work is ours.

Attributing vectors so a participant can verify their contribution to a specific report. Micropayments to thousands of wallets where fees do not eat the dividend. A revenue split of 80/10/10 baked into contract bytecode. Each of these components has off-the-shelf solutions on its own, the value is in the joints, in the circuit nobody assembled before us. That circuit is the company's engineering program for the next two years, and the round is sized to it.

The snowball

People pay for depth, and depth accumulates with time.

Ten weeks of vectors on a person read a cohort more accurately than one week. A hundred thousand participants expose cross-cohort patterns and connections between completely unrelated users that ten thousand would never reveal.
Every shipped report makes the next one more accurate: the accumulated mass of vectors is the raw material the forecasts are cut from.

More vectors, sharper insights, better reports, more buyers, more revenue, more contributors, more vectors. The snowball knows one direction.
The token follows the same logic. $HSIG supply is fixed at one billion, and there is no mint function in the bytecode: new tokens do not appear as the project grows.

Every payout is funded by B2B revenue through the built-in splitter and buyback, not by emission. The simple consequence: more reports and more participants mean more buyback volume against a fixed supply. That is arithmetic, not a price promise. A fixed asset with growing usage has one fundamental direction.
Whoever gets in early simply gets into that arithmetic earlier than the rest.

Status without a showroom

What already works: a public prototype on simulated data (a demo, labeled as such), Identity Vault on testnet, an open repository and a dev fund with on-chain reports on each milestone. What is in active development right now: the Telegram Mini App and its backend, the first viral phase of the project.

The role of the Mini App in the plan is specific: it is our viral distribution channel.
Telegram has already proven the scale of the channel: Hamster Kombat reported three hundred million players in a few months, whether you trust their counter or not, no other channel in consumer crypto scales like that. We just don't need the taps.

The Mini App hands a person a wallet, consent, and a referral link in thirty seconds, and turns Telegram's audience into protocol participants with a personal dashboard where they can share their link and lock a new participant to themselves in the peer-to-peer referral smart contract. That is enough while we build the second phase and the native app. Virality is the acquisition mechanic, depth arrives with the native app.

That is why we do not sell “cohort reports from the Mini App” as a finished product. Two thin signals are enough for the consent mechanic and for verifying the referral loop, but not enough for a full insight. Money will come where depth comes: in the native app with on-device vectorization. Reports to first buyers are an experiment on a live cohort to calibrate price before the main product, not a line in a business plan.

We do not put exact dates on the alpha until the backend has passed internal tests: a missed public deadline costs more than an unannounced one. $HSIG is a utility token, a vehicle for the user's share of revenue.

Where this adds up

The project is financed through a dev fund with public on-chain reports planned for each milestone: development progress can be verified without trusting anyone's mood, including mine.

Two questions lie ahead, and the market will answer both.

The first: is it willing to pay for consent instead of the old habit of taking data silently?
The second, the one that matters most to me: is a business ready to buy predictive insights about what is coming, rather than raw material for its own analysts?
However grandiose and unrealistic it may sound, that is the product.

For a business, this is an advantage of a particular kind: the sports almanac from Back to the Future, knowing results before they happen.

The difference is in method. We do not steal results from the future, we compute probabilities from live, consciously given signal.
Whoever sees demand a month before the check is written is talking to their market from a different position.

Whoever wants into this story from the beginning: the waitlist and all documents are at foremetric.ai.