Back to the scorecard

Neural and genetic methods consume most computation

Reference judgmentPartialDisputedResearch snapshot · October 15, 2026
Source confidence & research status
i
Editorial paraphrase · evidence v0.3. AI-assisted editorial assessments; original book editions not independently collated. Attribution and outcome confidence are recorded separately.

Source state: AUTHOR ADAPTATION CHECKED.

Attribution confidence
MEDIUM
Outcome confidence · deadline / review
MEDIUM / MEDIUM
Timing confidence / kind
LOW · UNESTABLISHED
Population
Technology markets and relevant users; no universality unless the forecast expressly requires it.

Our reading of the claim

Reference (stated reasonable reading)

Partial

Criterion: Neural networks and genetic algorithms account for more than half of computing work across the intended general computing population.

Deadline: 2019 · Included in this comparison

At original deadlineNOT FULFILLED
By review datePARTLY FULFILLED

Important neural successes by 2019 did not establish majority use across all computation. AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.

Deadline and follow-up notes

At deadline: Important neural successes by 2019 did not establish majority use across all computation.

By review: AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.

Evidence: N35 (opens in a new tab)N11 (opens in a new tab)N55 (opens in a new tab)

View this reading in the scorecard →
01

The forecast and its attribution.

EDITORIAL PARAPHRASE

Neural and genetic methods consume most computation

Spiritual Machines: The Merging of Man and Machine — Ray Kurzweil

1999 · Attribution / year unconfirmed
P02 2019 computing, majority-of-computations paragraph

AUTHORED ARTICLE THIRD PARTY MIRROR

Source publication: 1999 · Access: text checked

Attributed to Kurzweil; closing note identifies adaptation from The Age of Spiritual Machines (1999). An article mirror, not a scan of the book. Text transcription may contain errors. The article’s dollar amounts must not silently replace differently quoted book versions.

Catalogue entry P02
Open listed source (opens in a new tab)
Normalized claim
Neural and genetic methods consume most computation
Success criteria
Neural networks and genetic algorithms account for more than half of computing work across the intended general computing population.
Claim type
Adoption
02

Timeline

PREDICTION MADE1999Attributed forecast year; original edition not collated
ORIGINAL TARGET2019Deadline supplied by this assessment
FULL REALIZATION—No achievement year established

UNESTABLISHED: Not fully realized by 2026-10-15; overdue, not an observed delay

End of named target year.

Outcome at original deadlineNOT FULFILLED
Outcome at reviewPARTLY FULFILLED

Supplied target wording: 2019. The original deadline is preserved if a later forecast revises it. Forecast horizon: not established. Drift is a calendar-year difference, not a precise elapsed duration.

03

The evidence.

CONTEXT · Publication date not recorded

Krizhevsky et al.: ImageNet Classification with Deep Convolutional Neural Networks (opens in a new tab)

Primary institutional, research, or issuer source. Dates in the claim refer to the observation or product event, not the review date.. Primary institutional, research, or issuer source. Dates in the claim refer to the observation or product event, not the review date.

Source checked as of 2026-10-15
PRIMARY PAPER

Source publication: 2012 · Access: web text or indexed extract checked

Primary institutional, research, or issuer source. Dates in the claim refer to the observation or product event, not the review date.

Catalogue entry N35
CONTEXT · Publication date not recorded

NVIDIA: Blackwell GeForce RTX 50-series launch (opens in a new tab)

RTX 5080 launch MSRP $999, 1,801 AI TOPS. Launch prices are not represented as October 2026 retail quotes; a GPU also is not a whole PC.. RTX 5080 launch MSRP $999, 1,801 AI TOPS. Launch prices are not represented as October 2026 retail quotes; a GPU also is not a whole PC.

Source checked as of 2026-10-15
PRIMARY RESEARCH OR OFFICIAL

Source publication: 2025-01 · Access: web text or indexed extract checked

RTX 5080 launch MSRP $999, 1,801 AI TOPS. Launch prices are not represented as October 2026 retail quotes; a GPU also is not a whole PC.

Catalogue entry N11
CONTEXT · 2025-09-16

Google: Agent Payments Protocol AP2 (opens in a new tab)

Protocol and reference implementations; not evidence most real-world transactions use simulated people.. Protocol and reference implementations; not evidence most real-world transactions use simulated people.

Source checked as of 2026-10-15
PRIMARY RESEARCH OR OFFICIAL

Source publication: 2025-09-16 · Access: web text or indexed extract checked

Protocol and reference implementations; not evidence most real-world transactions use simulated people.

Catalogue entry N55

Capability, adoption, and timing

A related breakthrough is not automatically fulfillment. Review must check the actual capability, how widely it was required to be used, the specified technology, and the original deadline. A prototype cannot establish a claim about routine or widespread use.

i
Alternative interpretation: No invented count of all global operations. Reference PARTIAL reflects genuine importance plus an unestablished majority, not confidence that the majority is mathematically impossible.
04

Research notes & history

No invented count of all global operations. Reference PARTIAL reflects genuine importance plus an unestablished majority, not confidence that the majority is mathematically impossible.

Open questions

  • Original-edition collation remains an attribution task; it does not erase the stated outcome assessment.
Imported from
kurzweil_verification_v0_3_package/predictions.canonical.v0.3.json
Legacy identifier
None
Forecast family
K072
Parent statement
Not assigned
File checksum
93ae54d9ea2f1bd1817c481b85e2b707812bcd934c74d0f1715fa098d9b5f51b
Inspect preserved source fields
release_record
{"population_scope":"Technology markets and relevant users; no universality unless the forecast expressly requires it.","attribution_confidence":"MEDIUM","interpretation_confidence":"MEDIUM","outcome_confidence":{"at_deadline":"MEDIUM","at_review":"MEDIUM"},"timing_confidence":"LOW","date_uncertainty_affects_verdict":false,"interpretations":{"reference":{"criterion":"Neural networks and genetic algorithms account for more than half of computing work across the intended general computing population.","at_deadline":"NOT_FULFILLED","at_review":"PARTLY_FULFILLED","rationale":"Important neural successes by 2019 did not establish majority use across all computation. AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.","relaxed_or_changed_conditions":[],"kind":"REFERENCE_READING","documented_by_year":null,"at_deadline_rationale":"Important neural successes by 2019 did not establish majority use across all computation.","at_review_rationale":"AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.","evidence_source_ids":["N35","N11","N55"],"verdict":"PARTIAL","same_as_reference":false,"eligible_for_deadline_metrics":true,"deadline_year":2019,"outcome_assessment_complete":true},"strict":{"criterion":"Neural networks and genetic algorithms account for more than half of computing work across the intended general computing population.","at_deadline":"NOT_FULFILLED","at_review":"PARTLY_FULFILLED","rationale":"Important neural successes by 2019 did not establish majority use across all computation. AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.","relaxed_or_changed_conditions":[],"kind":"REFERENCE_READING","documented_by_year":null,"at_deadline_rationale":"Important neural successes by 2019 did not establish majority use across all computation.","at_review_rationale":"AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.","evidence_source_ids":["N35","N11","N55"],"verdict":"PARTIAL","same_as_reference":true,"eligible_for_deadline_metrics":true,"deadline_year":2019,"outcome_assessment_complete":true},"broader":{"criterion":"Massively parallel neural computation becomes central to frontier AI and a major driver of hardware.","at_deadline":"FULFILLED","at_review":"FULFILLED","rationale":"The narrower AI-industry population clearly satisfies central importance by 2019.","relaxed_or_changed_conditions":["All computing narrowed to frontier AI","Majority threshold narrowed to central importance"],"kind":"ALTERNATIVE_READING","documented_by_year":2019,"evidence_source_ids":["N35","N11","N55"],"verdict":"CORRECT","same_as_reference":false,"eligible_for_deadline_metrics":true,"deadline_year":2019,"outcome_assessment_complete":true}},"timing":{"first_full_achievement_year":null,"documented_by_year":null,"achievement_interval_years":null,"kind":"UNESTABLISHED","label":"Not fully realized by 2026-10-15; overdue, not an observed delay","drift_years":null,"drift_range_years":null},"deduplication":{"is_canonical":true,"is_repeated_identical_claim":false,"counting_unit":"DISTINCT_FORECAST_VERSION","note":"Different deadlines or materially different thresholds remain separate versions; identical legacy repetitions are linked, not recounted."},"revision_history":[],"is_new_in_v03":true,"counting_policy":{"unit":"distinct_forecast_version","identical_repetitions_count_once":true,"alternative_interpretations_count_once":true,"legacy_conditional_rows_count":false},"id":"K072","slug":"k072-neural-and-genetic-methods-consume-most-computation","title":"Neural and genetic methods consume most computation","prediction_paraphrase":"Neural and genetic methods consume most computation","wording_kind":"EDITORIAL_PARAPHRASE","original_quote":null,"primary_category":"AI & software","tags":[],"subcategory":"","claim_type":"ADOPTION","family_id":"K072","parent_statement_id":null,"legacy_ids":[],"origin":"NEW_SOURCE_EXTRACTION","publication":{"forecast_year":1999,"year_basis":"AUTHOR_ADAPTATION_OR_RETROSPECTIVE","original_book_edition_checked":false,"forecast_year_status":"ATTRIBUTED_1999"},"target":{"text":"2019","deadline_year":2019,"precision":"YEAR","year_start":2019,"year_end":2019,"interpretation":"End of named target year."},"source_verification":{"state":"AUTHOR_ADAPTATION_CHECKED","confidence":"MEDIUM","book_text_not_independently_collated":true},"success_criteria":"Neural networks and genetic algorithms account for more than half of computing work across the intended general computing population.","assessment":{"at_deadline_rationale":"Important neural successes by 2019 did not establish majority use across all computation.","at_review_rationale":"AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.","verdict_qualifier":null,"outcome_evidence_basis":"DIRECT_AND_CONTEXTUAL_EVIDENCE_WITH_EXPLICIT_EDITORIAL_INTERPRETATION","attribution_is_separate":true,"first_event_date_required_for_verdict":false,"at_review_semantics":"FULL_REALIZATION_HAS_OCCURRED_BY_REVIEW_NOT_NECESSARILY_CURRENT_PRODUCT_SALES","review_state":"EDITORIAL_ASSESSMENT_COMPLETE","verdict":"PARTIAL","as_of":"2026-10-15","at_deadline":"NOT_FULFILLED","at_review":"PARTLY_FULFILLED","confidence":"MEDIUM","disputed_interpretation":true,"benchmark_ready":true,"rationale":"Important neural successes by 2019 did not establish majority use across all computation. AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.","first_full_achievement_year":null,"full_achievement_documented_by_year":null,"drift_years":null,"drift_range_years":null,"still_unfulfilled_as_of_review":true,"eligible_for_deadline_metrics":true,"withheld_reason":null},"prediction_sources":[{"source_id":"P02","locator":"P02 2019 computing, majority-of-computations paragraph","role":"FORECAST_ATTRIBUTION"}],"evidence":[{"source_id":"N35","locator":"Primary institutional, research, or issuer source. Dates in the claim refer to the observation or product event, not the review date.","role":"OUTCOME_EVIDENCE"},{"source_id":"N11","locator":"RTX 5080 launch MSRP $999, 1,801 AI TOPS. Launch prices are not represented as October 2026 retail quotes; a GPU also is not a whole PC.","role":"OUTCOME_EVIDENCE"},{"source_id":"N55","locator":"Protocol and reference implementations; not evidence most real-world transactions use simulated people.","role":"OUTCOME_EVIDENCE"}],"limitations":"No invented count of all global operations. Reference PARTIAL reflects genuine importance plus an unestablished majority, not confidence that the majority is mathematically impossible.","open_questions":["Original-edition collation remains an attribution task; it does not erase the stated outcome assessment."],"legacy_relations":[]}

Review history

— Evidence v0.3: EDITORIAL_ASSESSMENT_COMPLETE; supplied verdict PARTIAL. Important neural successes by 2019 did not establish majority use across all computation. AI compute has become a major workload, but a global majority depends on the population and unit of work. The directional expansion is substantial; the literal aggregate threshold is not established.

Related forecasts.

No forecast family has been resolved for this record. Related statements and revised deadlines will be linked during research.