AI Catastrophe Probability & Safety Claims Analyzer

Existential Risk Probability & Consensus Mapping D3 Visual Cluster

Perceived Extinction Probability (p(doom)):

12.5%

Investigation Quick Presets:

Regulatory Intervention Scenario:

Active Slider Marker

Red Line (10% Marker)

AI Researchers & Frontier Labs

Public / Political Statements

Policy Impact Assessment Real-time Model

Projected Safety Gain

34.2%

Mitigation of catastrophic failure mode

Mitigation & Compliance Cost

$45.0B

Industry-wide compute & verification burden

Statement Analysis: IGN Breaking Report

IGN reported that Donald Trump has characterized AI safety warnings as a “hoax”, specifically dismissing claims that advanced artificial intelligence presents an existential hazard with a greater than 10% probability of causing human extinction.

“Donald Trump has called AI safety warnings a ‘hoax’ in response to claims the technology has a greater than 10% chance of killing all humans.” — IGN News Coverage (@IGN) & Public Debates

In contrast, consensus surveys among top AI researchers (including Yoshua Bengio, Geoffrey Hinton, and frontier lab leads at Anthropic) consistently locate existential risk (p(doom)) within the 10%–25% bracket, directly triggering standard emergency risk-management protocols.

Primary Grounding: IGN News Alert & Public Policy Records (2026-09-15)

Evidence Reference: AI Safety Institute Standards, Bengio & Hinton Statements, Anthropic RSP.

Catastrophe rubric: inputs are not measured probabilities

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Read the explanation

The safety analyzer takes a risk percentage as a user input; it does not estimate extinction probability from evidence. Its default twelve point five percent and mandatory-audit policy produce authored thirty four point two percent safety gain and forty five billion dollar cost. Gain increases by point five five for each input point above twelve point five, and cost by one point five billion. At the maximum fifty percent input, raw gain is fifty four point eight two five, displayed fifty four point eight, and cost one hundred one point two five, displayed one hundred one point three billion. Bars encode default and maximum gain captions at five pixels per point. These linear coefficients are assumptions without intervention trials, causal validation or uncertainty bands. Classification uses strict greater-than ten for high concern. Exactly ten remains in the moderate band, beginning at three, while below three is labeled low. Bars show boundary ten and default twelve point five at fifteen pixels per input point. Selecting a policy changes only gain and cost multipliers, not the entered risk or classification. Strict regulation multiplies default gain by one point two five to forty two point eight after rounding and cost by one point six five to seventy four point three billion. The code does not apply that gain to calculate a post-intervention extinction probability. Labels expressing policy recommendations are authored rubric output, not evidence that a particular intervention has the projected efficacy. The scatterplot contains ten manually authored entries with alleged risk percentages, quotations and impact scores. Their attributions and figures are not independently verified in this offline audit, and the impact-score axis has no empirical calibration. The user marker is always placed at ordinate fifty while its horizontal position follows entered risk; it does not measure impact. Bars use distinct labeled scales: eight pixels per point for the ten-point threshold and fifty-point marker, comparing unrelated plotting coordinates rather than measured impact. Missing D3 prevents the original chart but a guarded update preserves native metrics and JSON download. Four native resize widths compare original tool geometry and all footer placement; no public or exhaustive factual completion is claimed.