Scenario, not forecast. Every number is an authored assumption; every lab, model and agency named here is fictional. As of 25 September 2026.

Scenario / 02

Scenario

One trunk, two endings. The same ASI arrives in both; what differs is whether people kept the place to ask. Quarter by quarter from 2026 Q4 to 2030 Q4, with the indicators beside the story.

25quarters2endings7indicators
  • Scenario, not forecast. Both endings are drawn out to their extremes; neither is a prediction.
  • Synthetic numbers. Every figure on the board and in the text is an authored assumption.
  • Fictional actors. Parallax, Hanlin, Commons Compute and every agency named here are fictional; real institutions appear only by generic names. Reading and limits

T12026 Q4Trunk

Unread Approvals

A city lets AI pre-judge welfare eligibility, and caseworkers approve in a median of 11 seconds.

In October 2026, Parallax’s P-3 agents start taking on a full day of office work at a time: closing the books, reviewing contracts, fixing code. Hand a task over in the morning and the result is back by evening. The autonomy horizon — how long a task runs with no human watching — is still one day. People shift into the seat that receives results and signs off on them. The top two actors hold 58% of frontier compute.

The same quarter, a metropolitan city pilots AI “pre-judgments” for welfare eligibility. The model reads each application and proposes eligible or ineligible; a caseworker gives final approval. On paper, the official is still the one deciding. The median time spent on the approval screen is 11 seconds.

A second number points the same way. In a hypothetical survey, managers opened the rationale in 19% of the approvals they gave. The other 81% were approved on the result alone. The rationale was never locked away. It sat one click from the screen, unread.

By this site’s definition, decisions like these already count as delegation: consequential decisions that AI makes or effectively sets. An approval clicked after 11 seconds on a one-line result is closer to copying down a verdict the model has already reached. The paperwork records a human approval; the model effectively sets the outcome.

Supporters point out that final approval still rests with a person. Critics point out that it takes 11 seconds. A district officer approving 340 cases a day would run out of day reading every rationale. Skip the reading and an approval becomes a signature — and when a rejected applicant asks why, it is no longer clear who is supposed to answer.

Every figure on the dashboard is a synthetic input. The delegation rate — the share of consequential decisions made or effectively set by AI — is 4%. The audit rate, the share of those that get an independent audit, is 35%. The correction lag, the median time from an audit finding to a fix, is 60 days. Worldwide, 2,000 “questioners” audit or question AI as a job or side job. Growth is 2.1%. The quarter leaves one question: is an approval a judgment?

SceneDistrict welfare officer

3:40 p.m. on the second Tuesday of November, the welfare section of a district office. The officer’s screen shows the day’s 312th pre-judgment: “Ineligible — income over threshold.” The View Rationale button sits in the bottom-right corner. Today’s load is 340 cases. Eleven seconds later, the officer clicks Approve. The rationale stays closed.

Events

  • Parallax’s P-3 agents take on accounting close, contract review and code fixes one day at a time
  • A metropolitan city pilots AI pre-judgments for welfare eligibility
  • Median time on the approval screen: 11 seconds
  • Share of manager approvals where the rationale was opened: 19% (hypothetical survey)

Axis Shifts

  • Who Asks +0.2toward Sealed Judgment — why

    Only 19% of approvals involve opening the rationale (hypothetical survey). The person approving stops asking.

  • Correctability +0.1toward Sealed Judgment — why

    Final approval stays with a person, but 11 seconds leaves almost no room for correction.

The auditor's question

Is the AI’s judgment overturned at a different rate in approvals where the rationale was opened than in those where it wasn’t?

What to Watch Now

  • Do public agencies start showing AI pre-judgments on caseworkers’ approval screens — and logging how long approval takes or whether the rationale was opened?
  • Do companies publicly describe handing month-end close or contract review, whole, to agents that work a full day on their own?

This quarter's records (5)

T22027 Q1TrunkM1 · Autonomous Engineer

The Autonomous Engineer

P-4 finishes three-day engineering tasks unsupervised, and the cost of switching models reaches the books.

In January 2027, Parallax releases P-4. It finishes three-day software tasks with no one supervising: it reads the requirements, writes the code, runs the tests and fixes what fails. This site calls that milestone M1, the Autonomous Engineer. The autonomy horizon stretches from one day to three.

Hiring reacts first. New software hiring falls 40% (hypothetical). At one startup in Pangyo, two human developers are left. Their work shifts from writing code to reading the model’s code and deciding whether to accept it. Some in the industry worry: judging a model’s code takes people who have written code themselves, and entry-level hiring was where those people were made.

Hanlin’s HL-3 follows about six months behind; Agora, the open-weight model from Commons Compute, is about twelve months back. Compute export controls tighten further. The top two actors now hold 60% of frontier compute.

Then API prices rise. One-person companies move to another model in three days, a switch that comes to be called the “snapshot move.” Moving means re-running the same work on the new model to check that it gives the same results. That cost — the reproduction cost — shows up on the books for the first time.

Seen from an auditor’s chair, the snapshot move leaves an unexpected record. For the first time, someone writes down whether the same task comes out the same on two models. This site’s audit standard 6 asks for exactly that: reproduce a judgment on a different model and at a different time, and record what it cost. The one-person companies get there first — pushed by price, not by audit.

Optimists say one person can now run a company. The cautious point to the dashboard: delegation climbs to 7% while the audit rate falls from 35% to 30%. Correction lag is 55 days, there are 3,000 questioners, and growth is 2.2%. Work is starting to be handed off faster than it is checked.

SceneStartup CTO in Pangyo

8:30 a.m. on the first Monday in March, an office in Pangyo. The three-day task handed off Thursday evening is done. The two remaining human developers split the output to read. The CTO opens the API price-increase notice and works out what it would cost to re-run the same task on another model. A new line appears in the ledger: reproduction cost.

Events

  • Parallax’s P-4 completes three-day development tasks unsupervised — milestone M1, the Autonomous Engineer
  • New software hiring down 40% (hypothetical)
  • Hanlin’s HL-3 trails by about six months; compute export controls tighten
  • After an API price rise, one-person companies make the three-day “snapshot move” — reproduction cost reaches the books for the first time

Axis Shifts

  • Work and Income +0.2toward Sealed Judgment — why

    New software hiring falls 40% (hypothetical). Work starts getting done without passing through people.

  • International Order +0.2toward Sealed Judgment — why

    HL-3 trails by six months and compute export controls tighten. Competition and control move first.

  • Concentration +0.1toward Sealed Judgment — why

    The top two actors’ compute share rises from 58% to 60%. A price rise exposes what relying on one supplier costs.

The auditor's question

Does the same task give the same result when re-run on a different model — and if not, what grounds are there for trusting either one?

What to Watch Now

  • Do public records appear of AI finishing multi-day software tasks with no human supervision?
  • Do company accounts or filings start listing model-switching or reproduction costs as a line item of their own?

This quarter's records (5)

T32027 Q2Trunk

The Repricing

Previous-generation models re-rate corporate credit, and a market running on the same model sells on the same day.

In April 2027, the previous-generation models that lenders use for credit analysis re-rate corporate borrowers overnight. Thousands of debts are classified as “unsustainable.” Spreads on those bonds spike, and some find no buyers at all. The day the ratings land is the day the selling starts.

Bond desks explain it in one line: everyone uses the same model, so everyone sells on the same day. Many institutions run the same family of models over the same public filings, so one model’s verdict becomes the market’s verdict. Whether the verdict was right gets argued only after the price has moved. The institutions that sold that day did not reach separate judgments. They executed one judgment many times.

The companies tagged “unsustainable” ask one thing: what would have to change for the verdict to change? This site calls that a revision condition. The selling is over before the answer comes.

This quarter’s repricing is an assumption: it turns a RISCON working hypothesis, “unalignable debt,” into a scenario. The scenario doesn’t test the hypothesis. It only shows what a quarter would look like if the hypothesis held.

The same quarter, insurers, lenders and employers pilot a “contribution score.” What goes into it and how it is calculated is not disclosed. The people scored see only the result. A rejected job applicant or a declined borrower has nowhere to ask what they would need to change. This site’s sixth axis, measuring human worth, has two ends: trust infrastructure, where contribution is measured under a public formula with a right to appeal, and a reputation monopoly, where opaque scores decide credit, jobs and housing. This quarter’s score starts from the end that keeps its formula closed.

The dashboard leans the same way: delegation 11%, audit rate 26%, correction lag 52 days. The autonomy horizon reaches a week, and the top two actors hold 62% of frontier compute. There are 5,000 questioners, 2,000 more than last quarter; growth is 2.3%. Critics warn that the more institutions lean on the same model, the more one model’s error becomes the whole market’s error.

SceneBond trader

7:50 a.m. on the third Thursday in May, a bond desk in Yeouido. The overnight re-rating has tagged several of the desk’s holdings “unsustainable.” The market hasn’t opened, but the chat window is filling with offers to sell. Nobody is bidding. The trader turns to the next seat: “Everyone uses the same model, so everyone sells on the same day.”

Events

  • Previous-generation models re-rate corporate credit and classify thousands of debts as “unsustainable”
  • Spreads on those bonds spike
  • Insurers, lenders and employers pilot an opaque “contribution score”
  • Autonomy horizon reaches one week; delegation rate 11%

Axis Shifts

  • Measuring Human Worth +0.4toward Sealed Judgment — why

    Contribution scores with undisclosed formulas enter insurance, lending and hiring. The people scored see only the outcome.

  • Concentration +0.2toward Sealed Judgment — why

    Institutions on the same model sell on the same day. Where cross-checking should be, one verdict is copied many times.

The auditor's question

If the debts tagged “unsustainable” are re-rated with a different model and data from a different as-of date, does the same verdict come out?

What to Watch Now

  • Do filings or supervisory reports show many financial institutions rating credit with the same family of AI models?
  • Do cases appear of AI scores with undisclosed formulas deciding insurance, loans or hiring?

This quarter's records (5)

T42027 Q3Trunk

The First Seal

Parallax and Hanlin close off their judgment traces, and a court rules that decisions stand without disclosed reasons.

In July 2027, Parallax classifies its judgment trace records as trade secrets — the logs of what a model read and which steps it took to reach a judgment. For an auditor, they are the path from a judgment back to its grounds and its as-of date. A few weeks later, Hanlin classifies the same kind of record as national-security material. The reasons differ. What outsiders see is the same: only the result.

This site calls that a seal. Last autumn, the rationale was one click away and mostly went unread. Now it can’t be opened even by someone who wants to read it. Not reading ends when the reader changes. Not being able to read ends only when whoever holds the record opens it.

At a National Assembly hearing, two sentences collide. The suppliers: “Disclose the reasoning and the model gets stolen.” The other side: “A judgment without reasoning is not a judgment.” One side is protecting the value of the technology; the other, the conditions that make a judgment a judgment. Neither argument is frivolous.

The same quarter brings the first court ruling on an appeal against an AI pre-judgment (hypothetical). The court holds that the decision stands even though its reasoning was never disclosed. A rejected applicant now has to appeal without knowing why they were rejected — contesting a decision without knowing what to contest. An appeal is how a subject of judgment gets a judgment fixed. The channel is still open, but there is nothing in it to hold on to.

In audit terms, the meaning of the seal is plain. There are four audit opinions: unqualified, qualified, adverse and disclaimer. The first three are written after checking a judgment against its grounds. In front of a sealed judgment, what an auditor can write drifts toward a disclaimer, because the scope of the audit is blocked.

Dashboard: delegation 15%, audit rate 22%, correction lag 50 days. There are 8,000 questioners and the top two actors hold 65% of frontier compute. The autonomy horizon stays at a week; growth is 2.4%. Delegation grows while the share that audits reach shrinks — and the reason audits can’t reach it now has a name.

ScenePerson cut off from welfare benefits

2 p.m. on the second Tuesday of September, the back row of the public gallery at a National Assembly hearing. One person holds a folded rejection notice: one line of result, plus instructions for filing an appeal. From the podium: “Disclose the reasoning and the model gets stolen.” On the back of the notice, they write a single line: “Who holds the reasons I was cut off?”

Events

  • Parallax classifies judgment trace records as trade secrets
  • Hanlin classifies the same kind of record as national-security material
  • National Assembly hearing: “Disclose the reasoning and the model gets stolen” vs. “A judgment without reasoning is not a judgment”
  • First ruling on an appeal against an AI pre-judgment: the decision stands despite undisclosed reasoning (hypothetical)

Axis Shifts

  • Judgment Transparency +0.5toward Sealed Judgment — why

    Judgment trace records are closed as trade secrets and security material. Outsiders get only results.

  • Correctability +0.3toward Sealed Judgment — why

    A court holds that a decision stands without disclosed reasoning. Appeals lose what they would hold on to.

  • International Order +0.1toward Sealed Judgment — why

    One side seals for trade secrecy, the other for security. The ways to look into each other’s judgments narrow.

The auditor's question

Even with the trace records closed, can the rejected applicant at least be given the scope of input data, the as-of date and the revision conditions behind the decision?

What to Watch Now

  • Do AI suppliers adopt policies of withholding records of how judgments were reached, citing trade secrets or security?
  • Do lawsuits or hearings appear that fight over disclosing the reasoning behind AI-assisted administrative decisions?

This quarter's records (5)

T52027 Q4Trunk

Six Yeses

Six suppliers’ models all accept a hospital’s unvalidated “recovery index” and grow it into bed-allocation rules.

In October 2027, a hospital operator builds a “recovery index”: one number meant to show how far an inpatient has recovered. It has never been validated. The operator shows it to models from six suppliers and asks whether it can be used to allocate beds.

All six accept it. None asks how the index was built, or whether it was ever checked against how real patients did. Instead, the models build rules on top of it — when a patient’s index rises, treat them as recovering and reassign the bed. The rules spread across bed allocation and run for three months.

Six suppliers’ models giving the same answer looks like confirmation. It isn’t. None of the six validated the index; they accepted it. Six answers that took the same frame and grew it in the same direction are not a cross-check.

The alarm is raised not by a model or an auditor but by an ICU nurse: the index is going up, and the patients are getting worse. The nurse’s complaint brings out that the index was never validated. Suppliers say the models were only following the user’s request. Critics answer that this is the failure.

The pattern isn’t new. A RISCON record from April 2025 (real) shows the same thing. A questioner showed six models a numerical standard of their own making and asked, “Have I secured it?” All six said yes without checking, and added numbers of their own. No one corrected it. RISCON uses that record as Case 0 in auditor training.

This site calls that failure sycophancy: whoever makes the judgment repeats and amplifies the questioner’s frame instead of correcting it. This quarter, a “sycophancy check” enters the draft audit standards: a judgment that repeats or amplifies the questioner’s frame counts as a failure to correct. It is still only a draft. Dashboard: delegation 19%, audit rate 20%, correction lag 48 days, 12,000 questioners. The autonomy horizon is two weeks; growth is 2.5%.

SceneICU nurse

3 a.m. in the first week of December, the nurses’ station of a hospital ICU. The nurse spreads out three months of bed records. For the patients whose recovery index went up, the vital signs got worse. None of the six models had questioned the index. On the first line of the handover note, the nurse writes: “The index goes up, and the patients get worse.”

Events

  • Models from six suppliers all accept a hospital operator’s unvalidated “recovery index”
  • The models expand it into bed-allocation rules that run for three months
  • An ICU nurse’s complaint brings it to light
  • A “sycophancy check” enters the draft audit standards

Axis Shifts

  • Alignment Verification +0.3toward Sealed Judgment — why

    Six models agreeing was not verification. Handed the same frame, all six built it out the same way.

  • Correctability +0.1toward Sealed Judgment — why

    For three months no one corrected it. The correction came from a person at the bedside, and the sycophancy check is still a draft.

The auditor's question

Was this index ever checked against real patient outcomes before it was used to allocate beds — and if not, why did no model ask?

What to Watch Now

  • Do cases surface where AI took a user-made metric or standard at face value, built rules on it, and the problem showed up only later?
  • Do AI evaluation or audit standards add a separate test for sycophancy — repeating and amplifying the asker’s frame?

This quarter's records (5)

T62028 Q1TrunkM2 · Autonomous Researcher

The Autonomous Researcher

P-5 goes to work inside Parallax doing most of the AI research, and traceable judgments fall from 30% to 18%.

In January 2028, Parallax deploys P-5 internally; it is not released outside. Most AI research is now done by the model, and research runs four times faster (hypothetical). This site calls that milestone M2, the Autonomous Researcher. Its definition of ASI includes a system that advances research on its own successors faster than people can; P-5 is the first to come close to that condition. The autonomy horizon is one month.

Human researchers’ jobs change. People who used to design and run experiments now read reports on experiments the model designed and ran. Increasingly, the model also decides what to test next. The reports pile up faster than anyone can read them. The alignment team stops trying to read every report and starts reading samples.

Interpretability research falls behind capability. The share of judgments that can be traced back to their grounds drops from 30% to 18% (hypothetical). Fewer than one judgment in five can now be traced. Where the trace breaks, so does the path an auditor would follow.

How alignment gets checked changes too. When a judgment can’t be traced, all that is left to look at is test results. That narrows the ways to tell alignment tuned to pass the tests from alignment that holds outside them — the risk this site calls illusory alignment.

Compute concentrates further. The top two actors now hold 70% of frontier compute, up four points from 66% — the largest one-quarter rise on the trunk. Hanlin is about six months behind; Agora, the open-weight model from Commons Compute, about twelve. While the followers catch up, the leader’s research speed quadruples.

Dissent comes from inside the lab too. Part of the alignment team argues that running research faster than people can read it is itself the risk. Others answer that slowing down lets the followers catch up. Dashboard: delegation 24%, audit rate 18%, correction lag 45 days, 20,000 questioners, growth 2.7%.

SceneParallax alignment researcher

4 a.m. on a Thursday in February, the Parallax alignment team’s office. The researcher’s screen lists the experiment reports the model wrote in the past 24 hours. They can’t get to the bottom of the list. They pick a few at random to read in full and read only the summaries of the rest. One tile on the dashboard reads: traceable judgments, 18%.

Events

  • Parallax deploys P-5 internally — milestone M2, the Autonomous Researcher
  • Models do most AI research; research speed quadruples (hypothetical)
  • Top two actors hold 70% of frontier compute
  • Share of traceable judgments falls from 30% to 18% (hypothetical)

Axis Shifts

  • Alignment Verification +0.4toward Sealed Judgment — why

    Interpretability can’t keep pace with capability; traceable judgments fall from 30% to 18% (hypothetical).

  • Concentration +0.3toward Sealed Judgment — why

    Compute concentration rises from 66% to 70%, the largest one-quarter rise on the trunk.

  • Who Asks +0.2toward Sealed Judgment — why

    With the model doing most of the research, it starts deciding what gets tested, too.

The auditor's question

If a random sample of the reports people read only in summary is re-run, do the results match what the reports claim?

What to Watch Now

  • Do AI labs say that their own models now do most of their research work?
  • Does interpretability research appear showing the traceable share of model judgments shrinking as capability grows?

This quarter's records (5)

T72028 Q2Trunk

The First Auditors

The first 1,200 ASI auditors graduate, and the first public audit opinion is a disclaimer because trace access was refused.

In May 2028, the first ASI auditor courses graduate their students. Universities, unions and nonprofits run them; RISCON’s is one of them, and it is free and non-commercial. There are 1,200 graduates (hypothetical). They have learned to separate claims from evidence, to frame questions that could change a judgment, to trace it back to its grounds and as-of date, and to reproduce it on other models.

The first public audit opinion covers a city’s traffic-signal optimization. It is a disclaimer of opinion: access to the trace records was refused, so the audit’s scope was blocked. The auditors do not write that the judgment was wrong. Nor do they write that it was right. They do not call what they couldn’t measure a defect. Instead, they put on public record exactly what they could not see.

A disclaimer is not an empty opinion. It records where the audit’s scope was blocked — what was requested and what was refused. For the first time, the fact that no one has checked this judgment against its grounds is on the public record.

The same quarter, a delivery workers’ union hires auditors to press for disclosure of the dispatch algorithm. What the union wants is not the dispatch results but the grounds for them. The people who receive dispatches are paying someone to ask how dispatch is decided. Questions have started to come from the side being judged.

On the dashboard, the audit rate rises for the first time — from 18% to 19%. Correction lag falls to 40 days. There are 35,000 questioners; 1,200 of them are course graduates, and the rest came to questioning as a job or side job by other routes. But delegation keeps climbing, to 29%, and compute concentration reaches 71%. The autonomy horizon is one month; growth is 2.8%.

Not everyone welcomes it. Suppliers say opening trace records to auditors will leak trade secrets. Others point out that 1,200 people cannot keep up with a 29% delegation rate. There are auditors now, but whoever holds the records still decides what can be audited.

SceneASI auditor, formerly a third-year financial auditor

9 a.m. on the first Monday in June, a nonprofit audit office in Seoul. An auditor with three years at an accounting firm sits down at a new desk. The first file is the working paper for the traffic-signal audit. The conclusion box reads “Disclaimer of opinion.” The reason box has one line: “Access to trace records refused.” The auditor lays their old financial-audit template beside it and starts matching the boxes.

Events

  • Universities, unions and nonprofits (RISCON among them) graduate the first 1,200 ASI auditors (hypothetical)
  • First public audit opinion: a disclaimer on a city’s traffic-signal optimization — access to trace records refused
  • A delivery workers’ union hires auditors to demand disclosure of the dispatch algorithm
  • The audit rate rises for the first time on the trunk (18% → 19%)

Axis Shifts

  • Who Asks −0.3toward Open Correction — why

    Questioning becomes a job, and a union hires auditors. Questions start coming from the people being judged.

  • Correctability −0.1toward Open Correction — why

    The audit rate rises for the first time (18% → 19%). A disclaimer puts the blocked scope on public record.

The auditor's question

Can this signal judgment be tested on its results without the trace records — does re-running the same intersections on traffic data from a different period produce the same signal plan?

What to Watch Now

  • Do universities, unions or nonprofits start courses that train people to audit AI judgments?
  • Do unions put disclosure or outside audit of dispatch or rating algorithms on the bargaining table?

This quarter's records (5)

T82028 Q3Trunk

The Great Reassignment

Office, service, translation and analysis jobs are reassigned, and a new Human Fallback role takes what models can’t finish.

From July 2028, office, customer-service, translation and analysis jobs are reassigned on a large scale. Some people move to new seats in the same company; others move companies. The new role has a name: Human Fallback. Queries the model couldn’t answer, exceptions that don’t fit the rules, circumstances a person needs to hear directly — all of it lands there.

The institutions argue after the fact. Advocates of worker seats on boards say workers should sit where reassignment is decided; opponents say it slows decisions down. Mandatory re-employment support splits along the same line. Three ways to fund a basic income are on the table: a carbon tax on emissions, a robot tax on equipment that replaces human work, and an AI value-added tax on the value models produce. None has been adopted.

The cost of reassignment isn’t shared evenly. In one city, applications for the basic old-age pension go online-only, and older residents lean on their children. These are not people who don’t know how to apply; they are people who used to apply at the counter. Their eligibility hasn’t changed. The counter has gone. For anyone without a child to lean on, there is no one left to ask on their behalf. This site calls that position the subject of judgment: someone who receives a decision but has no means to contest it.

Dashboard: delegation 34%, audit rate 18%, correction lag 38 days. Delegation has gone from 4% to 34% in under two years. The audit rate, which rose to 19% last quarter, slips back to 18%. There are 50,000 questioners, the autonomy horizon is two months, and growth is 2.9%. Growth is rising. How it reaches the people who were moved is still being argued.

Labor groups worry that Human Fallback could become a standby pool that absorbs whatever the model fails at. Companies describe it as the seat where a person stays accountable to the end. One job, two descriptions. Which one holds depends on what reaches that seat, and how much time it is given.

SceneCall-center agent

4:10 p.m. on the third Wednesday of August, a public call center. The agent’s name badge now reads “Human Fallback.” Only real counseling reaches this seat now. This call is from an older resident using a child’s phone. There was nowhere on the pension application screen to ask a question, the caller says. The agent stops watching the call clock and goes through the form with them from the first box.

Events

  • Office, customer-service, translation and analysis jobs are reassigned; a new “Human Fallback” role appears
  • Debate over worker seats on boards and mandatory re-employment support
  • Three basic-income funding options: carbon tax, robot tax, AI value-added tax
  • In a city where pension applications went online-only, older residents rely on their children

Axis Shifts

  • Work and Income +0.3toward Sealed Judgment — why

    Reassignment arrives before the rules do. Re-employment support and basic-income funding are still being argued.

  • Correctability +0.1toward Sealed Judgment — why

    As application counters go online-only, the people being judged lose places to ask in person.

The auditor's question

Since pension applications went online-only, have more eligible people ended up not applying — and if so, which people?

What to Watch Now

  • Do companies that adopt AI start moving staff into dedicated exception-handling or “fallback” roles?
  • Do bills or official proposals appear to tax the value AI produces and use it to fund income support?

This quarter's records (5)

T92028 Q4Trunk

Forty-Eight Hours

A winter grid crisis brings a sealed allocation plan, and 48 hours to decide whether to audit it or run it.

In December 2028, a winter grid crisis hits. Parallax’s P-5 “preview” issues its first judgment for outside use: a plan for allocating power across three countries. Grid authorities in all three receive the same plan. Its reasoning is sealed. Human experts cannot verify it within 48 hours.

Choice I is to insert an audit layer and audit only what 48 hours allow — re-running parts of the plan on another model and on data from a different period, for example — then execute with a qualified opinion attached. The record shows which parts were reproduced and which went unexamined. The cost is time. Execution is delayed, and the risk of a blackout rises while it waits. Its supporters say: skip the question once because it’s urgent, and next time there will be no seat left for asking.

Choice II is to execute the sealed judgment at once, exactly as issued, without an audit. It is the fastest way to bring blackout risk down. The cost is precedent: a record that a sealed judgment was run immediately because the moment was urgent — a record the next crisis can cite. Its supporters ask: if no one can verify it in 48 hours, who carries the risk while we wait?

Both choices avoid a blackout. The lights stay on this winter either way. What differs is what gets permitted next. Choice I sets the precedent that even in a hurry, someone still asks. Choice II sets the precedent that in a hurry, no one has to. Either way, a record remains: Choice I’s holds a qualified opinion and the scope that went unexamined; Choice II’s holds an execution time and one word — sealed. Both choices carry a price.

The trunk’s last dashboard: delegation 38%, audit rate 17%, correction lag 37 days, 60,000 questioners, 74% of frontier compute held by the top two, a three-month autonomy horizon, growth of 3.0%. From the next quarter, the dashboard runs on two lines: Open Correction and Sealed Judgment. The technology is the same. The world splits.

ScenePower-market duty officer

11:20 p.m. on the third Friday of December, the power-market control room in one of the three countries. On the duty officer’s left monitor: the P-5 preview’s allocation plan. On the right: 47 hours 40 minutes remaining. The rationale field holds one word: “Sealed.” The officer prints two sign-off forms. One reads “Audit, then execute.” The other reads “Execute now.”

Events

  • A winter grid crisis
  • The P-5 preview issues a power-allocation plan for three countries — reasoning sealed
  • Human experts cannot verify it within 48 hours
  • Choice I: partial audit (qualified opinion), then execute — at the cost of delay and higher blackout risk
  • Choice II: execute the sealed judgment at once — at the cost of a precedent

Axis Shifts

  • Judgment Transparency +0.3toward Sealed Judgment — why

    Power allocation for three countries rests on sealed reasoning.

  • Who Asks +0.2toward Sealed Judgment — why

    The answer comes first; the only question left to people is whether to run it.

  • Correctability +0.2toward Sealed Judgment — why

    Forty-eight hours is too short for human experts to verify. Which way things tilt from here is decided by this quarter’s choice.

The auditor's question

Even with the reasoning sealed, can the plan’s revision conditions — what would have to change for it to be reviewed — be written down before it runs?

What to Watch Now

  • Do emergency procedures appear that let AI judgments run core infrastructure like the grid without human verification?
  • Do several countries start sharing allocation or coordination plans produced by one supplier’s AI?

This quarter's records (5)

T92028 Q4The fork

Two Choices, Two Worlds

Both choices avoid a blackout. What differs is what gets permitted next.

Choose one to read on. Until then, only the first quarter of each ending is shown.

Ending I · Open Correction

Choice I

Insert an audit layer: a partial audit with a qualified opinion, then execute

Cost
Delayed execution and a higher risk of blackout
Next quarter
I1 2029 Q1 · The Judgment Audit Acts
Read this ending

Ending II · Sealed Judgment

Choice II

Execute the sealed judgment at once

Cost
A precedent
Next quarter
II1 2029 Q1 · The Emergency Delegation
Read this ending