Google Gemini surpasses ChatGPT in monthly active users by Q4 2026?
Market 46.0% against model 52.0%. Resolves in 188d 10h, data updated 13d ago.
Decision layer
Watchlist candidate
The market is worth monitoring, but the current edge or evidence does not justify an actionable label.
Expected value after costs, not raw probability spread.
How much support the model sees across available inputs.
Thin markets can erase apparent edge through spread and slippage.
Resolution ambiguity, timing, and data quality pressure the decision.
weak feature coverage.
Why / why not trade
One decision layer for the market read.
This public box mirrors the internal diagnostic style without exposing execution controls: decision, probability gap, cost-adjusted edge, blocker, and next thing to monitor.
side YES
52.0% model / 46.0% market
fees, spread, slippage, risk
Interesting disagreement, but the full action threshold is not met.
Watch resolution risk, timing, and data quality before trusting the gap.
Read this market in three passes
Model 52.0% vs market 46.0%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
Watch, do not force it
Why this read matters
The market is directionally interesting, but at least one evidence, edge, liquidity, or risk condition is not strong enough.
Google Gemini surpasses ChatGPT in monthly active users by Q4 2026?
Volume $90,000
Market-implied vs model probability
Factor attribution
The model estimates a 6-point higher probability than the market, primarily driven by historical base rate.
| FACTOR | SIGNAL | WEIGHT | LOG-ODDS ΔLog-odds contribution measures how much each factor shifted the model's probability estimate in log-odds space — the mathematically correct way to stack independent evidence. Formula: Δlog-odds = weight × signal. Positive values push the probability up; negative values push it down. Log-odds are converted back to probability via the logistic function at the end. | DIRECTION | DESCRIPTION |
|---|---|---|---|---|---|
| Historical base rate | 42% | — | −0.323 | Bearish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
| Model probability | 52.0% | Prior: 42% · Market: 46.0% | |||
| Confidence (λ)Confidence λ (lambda) controls how much weight to give the model vs. the market. Formula: p_final = λ·p_model + (1−λ)·p_market. λ is derived from data quality, factor agreement, and liquidity. When inputs are weak, the model shrinks toward the market — not toward 50%. | 0.59 | Final: 52.0% = λ·model + (1−λ)·market | |||
Comparable eventsseeded prior 42% - 0 matches (min 8 for historical)
No comparable events matched for this market.
Scenario treeEngine template
Node probabilities are conditional on the parent; hover for cumulative path probability. Leaf EV is per $1 YES contract at the current price, before fees (fee-adjusted EVs in the table on the left).
| Path | Path prob. | YES pays | EV (YES, after costs) |
|---|---|---|---|
| Threshold hit in first half of window | 23.4% | $1 | +50.5c |
| Threshold hit in second half | 28.6% | $1 | +50.5c |
| Never reaches threshold in window | 48.0% | $0 | -49.5c |
Root-implied probability 52.0% reconciles with the model's 52.0% (±1pt invariant).
Description
AI assistant market share by year-end. Hard to forecast — model sees slim edge.
Resolution criteria (verbatim, with analyzer flags)
analyzed by heuristicResolves YES if credible third-party reports (SimilarWeb, Sensor Tower, or equivalent) show Google Gemini exceeding ChatGPT in global monthly active users for any month in Q4 2026.
- Subjective judgment Resolution depends on someone's judgment call rather than an observable fact.
Resolves Fri, 01 Jan 2027 03:48:52 GMT. The contract pays on these exact criteria, not on the thesis.
Suggested paper position
Paper position only. No real-money execution
Live open-market tracking
Since the first stored model read on 2026-06-09, the market has moved from 46.0% to 46.0%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Data quality58/100 - weak
When features are unavailable, the model increases uncertainty and weights the final estimate closer to the market price. Lower data quality does not mean the market is wrong. It means the model is being appropriately humble.
Risk factor breakdownsim
| Inverse liquidity | 40 | |
| Price volatility | 40 | |
| Resolution proximity | 0 | |
| Data quality | 64 | |
| Category base risk | 60 | |
| Resolution ambiguity | 28 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 46/100, higher = riskier.