We verify claims about generative AI, from ChatGPT's record-breaking user growth to the technical specifications of large language models and cloud databases.
215 Tech claim verifications avg. score 5.9/10 118 rated (mostly) true 77 rated (mostly) false
“AWS Database Migration Service homogeneous migrations from PostgreSQL can migrate source database schema objects to a compatible PostgreSQL target.”
AWS documentation clearly confirms this capability. DMS homogeneous PostgreSQL migrations use native PostgreSQL tools such as pg_dump and pg_restore to move schema objects to compatible PostgreSQL targets, including supported Amazon RDS and Aurora configurations. Compatibility requirements and object-specific limitations may still apply.
“GPT-4 had a context window of 128,000 tokens at its release in March 2023.”
The March 2023 release did not provide a 128,000-token context window. OpenAI documented an 8,192-token window for GPT-4 and limited access to a 32,768-token variant; the 128K window was announced for GPT-4 Turbo in November 2023. The claim conflates the original model with that later release.
“E-E-A-T is a framework used by Google's search quality evaluators to assess the value and reliability of online content.”
Official Google guidance confirms that search quality raters use E-E-A-T considerations when assessing page quality, including whether content is trustworthy and reliable. Calling E-E-A-T a “framework” is reasonable, although Google more often describes it as criteria or considerations. These evaluations help assess search-system performance but do not directly set page rankings.
“Large language models generate text by predicting likely word sequences from patterns learned during training rather than inherently retrieving verified facts from a database.”
The description accurately captures how standard large language models generate text. They predict tokens from learned statistical patterns and may encode factual associations in their parameters, but they do not inherently consult a verified factual database. External retrieval systems can add database or document access, while “word sequences” is a reasonable simplification of token prediction.
“Research by NP Digital found that more than 43% of marketers reported that false or hallucinated AI-generated information passed review and was published publicly.”
NP Digital published the quoted “over 43%” finding, but its study materials and related coverage do not present a consistent statistic. Most reports identify 36.5% as the share of marketers whose incorrect AI content reached the public, while 43.5% may describe an error category instead. The attribution is genuine, but the claimed percentage is not reliably established.
“OpenAI's ChatGPT reached one million users within five hours of its launch.”
ChatGPT reached one million users in about five days, not five hours. Contemporaneous statements and subsequent reliable reporting consistently give the five-day timeframe, while none of the cited evidence supports five hours. The incorrect time unit creates a 24-fold error in the claim’s central quantitative assertion.
“Python 3.12 runs about 20% faster than Python 3.11 on the official Python benchmark suite.”
Official benchmarks do not support a 20% speedup. Python 3.12 was reported as roughly 4–5% faster overall than Python 3.11, with results varying by workload and platform. The larger figure appears to confuse this comparison with Python 3.11’s performance gain over Python 3.10.
“OpenAI released GPT-4 in March 2023.”
Reliable primary documentation and independent news reporting confirm that OpenAI released GPT-4 in March 2023. Most sources identify March 14 as the announcement and initial rollout date; reporting dated March 15 reflects publication timing or the continuing rollout, not a substantive contradiction.
“ChatGPT reached 100 million monthly users within two months of its launch.”
The milestone is well supported as a widely accepted estimate. UBS analysis based on Similarweb data placed ChatGPT at roughly 100 million monthly active users in January 2023, about two months after its November 30, 2022 launch. However, the figure was not an official OpenAI count, and extensive media repetition largely traces back to that same underlying analysis.
“OpenAI was founded in 2015 as a nonprofit research laboratory.”
Corporate records and contemporaneous reporting establish that OpenAI was founded in December 2015 as a nonprofit artificial-intelligence research organization. Reports that some operations began in early 2016 do not change its 2015 incorporation and public launch, while later restructuring does not alter its founding status.
“ChatGPT's growth to 100 million monthly users within two months was the fastest adoption of any consumer application in history.”
ChatGPT did reach an estimated 100 million monthly active users roughly two months after launch and was considered the fastest-growing consumer application at that time. The enduring historical superlative is outdated because Threads later reached 100 million registered users in five days. Those measurements are not perfectly equivalent, but the claim supplies no qualification preserving ChatGPT’s former record.
“Mailchimp's Standard plan starts at $20 per month.”
The advertised entry price is accurately stated. Multiple current pricing sources place Mailchimp’s Standard plan at $20 per month for the lowest paid contact tier, while Mailchimp confirms that pricing scales with contact count. Actual bills can be higher because of larger audiences, add-ons, taxes, or overage charges.
“The unnamed new platform delivers open rates 40% higher than the email marketing industry average.”
No cited evidence verifies that a specific new platform delivers a 40% open-rate uplift. The similar figure found in vendor marketing concerns AI tools generally, not measured results for this platform. Because published industry averages also vary substantially, the comparison lacks both a verified result and a defined baseline.
“Spotify launched in the United States in July 2009.”
Spotify did not launch in the United States in July 2009. Contemporaneous reporting and launch announcements establish that the service arrived in the U.S. on July 14, 2011, following years of anticipation. The limited 2009 material either predicted a future launch or carries inconsistent dating.
“Python 3.12 runs about 60% faster than Python 3.11 on the official benchmark suite.”
Official Python materials estimate that Python 3.12 is about 5% faster than 3.11 overall, not 60%. The larger figure belongs either to Python 3.11’s gains over 3.10 or to isolated workload-specific improvements. Independent benchmarks also indicate modest, uneven gains rather than a suite-wide 60% increase.
“Python 3.12 was released on October 2, 2023.”
Official Python records confirm October 2, 2023, as the release date of Python 3.12.0, the first final release in the 3.12 series. Release documentation, schedules, download records, file timestamps, and contemporaneous announcements consistently support that date.
“Python 3.12 removed the distutils package from the Python standard library.”
Official Python documentation confirms that Python 3.12 removed distutils from the standard library, as planned under PEP 632. Setuptools may still provide a compatible, third-party implementation, but that does not affect the claim’s accuracy.
“Mailchimp's Standard plan starts at US$20 per month.”
Mailchimp lists its Standard plan with a US$20 monthly starting price, corroborated by multiple recent independent pricing reviews. That entry price applies to the lowest contact tier; costs increase with contact volume, and displayed terms may include a 12-month pricing condition.
“OpenAI released GPT-4 on March 14, 2023.”
Contemporaneous evidence confirms that OpenAI released GPT-4 on March 14, 2023. OpenAI’s announcement is independently corroborated by Reuters, Bloomberg, and other major outlets. Access was initially phased, but that does not alter the official release date.
“As of August 27, 2026, artificial intelligence cannot replace a talented graphic designer because artificial intelligence is not good at visual work.”
The statement reaches a defensible conclusion about full-role replacement but gives the wrong reason. AI can already produce strong visual work and has outperformed a human designer on one live advertising metric. Its remaining limitations are concentrated in compositional precision, consistency, typography, brand strategy, emotional nuance, and contextual judgment—not visual work generally.