Predictive History YouTube Channel Earnings Report & Research

To answer how much does Predictive History make, Branzly runs the channel's estimated 1.7M monthly views through the same ad revenue calculator used across the site: a Education RPM of $2.7-$6.8 from 2026 niche benchmarks, with the headline figure taken at the middle of that range ($4.8), the same RPM used in the home page calculator. That puts Predictive History YouTube ad revenue at roughly $4,677 to $11,778 per month, or $56,118 to $141.3K per year from AdSense alone.

RPM is what the channel actually keeps per 1,000 views after YouTube's 45% revenue share, unfilled ad inventory and ad blockers, which is why these Predictive History earnings figures sit far below the advertiser-side CPM quotes you may see elsewhere. Views from Tier 1 countries such as the United States, United Kingdom, Canada and Australia pay the most, while Shorts views pay a fraction of long-form RPM.

On top of ad revenue, we assume one integrated-mention sponsorship per month priced on 566.0K average views per video at a Education cost-per-view rate of $0.01 with a 1.2× niche multiplier — the identical model in our sponsorship calculator. That suggests a Predictive History sponsored video is worth about $6,846 to $13,693, with a fair midpoint near $9,781.

Sponsorship pricing follows views and engagement, not subscriber count, so a large channel with a quiet video is still only paid for the views that video earns. Brands asking "how much does it cost to sponsor Predictive History?" should expect dedicated videos to cost two to three times an integrated mention.

Channel-level deals work differently again. A retainer is an ongoing fee for a set of deliverables each month, not the sum of every slot the channel could sell, so pricing follows published size bands. With 3.0M subscribers, Predictive History sits in the Macro (1M-5M subs) band, where retainers typically run $18,000 to $40,000 a month. Niche demand, engagement, cadence across 3 uploads a month and how much of the audience watches each upload decide where in that band a deal lands. Spread back across uploads, that adds about $9,810 of value to each video.

Put simply, Predictive History YouTube earnings are estimated at roughly $32,124 to $65,471 per month when ad revenue, one sponsored integrated mention and a retainer are combined, or about $385.5K to $785.6K per year. Channels that publish more sponsored videos, sell merchandise or courses, run memberships, or earn affiliate commissions typically make more than this range.

Net worth estimates for YouTubers like Predictive History usually start from several years of this yearly income, minus taxes, team costs and production spend. Treat any Predictive History net worth figure as a planning range, not a disclosed number.

Predictive History (@predictivehistory) is a education YouTube channel that has published 195 videos and accumulated 110,370,557 lifetime views from an audience of 2,950,000 subscribers. The channel was created in June 2023, which makes it roughly 3 years and 5 months old, and it continues to publish content for viewers interested in education topics. In the creator's own words: "In his Foundation series, Isaac Asimov proposed that the science of "psycho-history" will help humanity understand its past, predict its future, and control its present. This channel is dedicated to exploring if "psycho-history" is indeed possible. This channel seeks to answer the following three questions: 1. What models, theories, and paradigms help us best understand world history? 2." Branzly analyses Predictive History using public YouTube data so creators, brands and media buyers can understand the channel's size, output and likely earning power before they reach out, benchmark against it, or plan a collaboration.

Predictive History has been on YouTube for approximately 3 years and 5 months, starting in June 2023. Across that period the channel has averaged around 65 uploads per year, which gives a useful signal about publishing consistency: channels that maintain a steady cadence typically build stronger session watch time and more predictable sponsorship inventory than channels that upload in bursts. Longevity also matters for advertisers, because an established back catalogue keeps generating views long after each video's launch week. With 195 videos in the library, Predictive History earns a meaningful share of its monthly views from older content rather than from new uploads alone, and that evergreen tail is one reason estimated revenue for education channels can stay stable between releases.

Predictive History currently has 2,950,000 subscribers, a figure that places the channel in the million-subscriber tier of YouTube creators. Total channel views stand at 110,370,557, and average views per video sit at approximately 566,003. The ratio of lifetime views to subscribers is about 37.4 views for every subscriber, which indicates an audience that is concentrated in the channel's regular, subscribed viewers. Branzly estimates recent activity at roughly 1,732,041 views per month based on the channel's most recent uploads and publishing history, which is the number our ad revenue calculator uses to model earnings.

The most-watched long-form video currently visible for Predictive History is "Game Theory #25: Trump Visits China" with 1,782,320 views. The channel has no recent Shorts in the sampled uploads, so its reach is driven mainly by long-form content. Top-performing titles are worth studying because they reveal which hooks, formats and topics the algorithm rewards for this audience. For brands, they also show the realistic ceiling of a sponsored placement: a dedicated integration on a channel of this size will usually land somewhere between the average video performance of 566,003 views and the outliers shown above.

Based on an estimated 1,732,041 monthly views and typical education CPM ranges, Predictive History could earn roughly $4,676 to $11,777 per month from YouTube ad revenue, or approximately $56,118 to $141.3K per year. These figures reflect the AdSense share only. Most channels of this size also earn from brand sponsorships, affiliate links, memberships, merchandise and product sales, so total creator income is commonly several times the advertising estimate. Sponsorship value is usually priced per view rather than per thousand impressions, and education audiences typically command mid-range rates because of advertiser demand in that vertical.

Branzly classifies Predictive History under the Education category using the channel's description, keywords, topic signals and the language of its best-performing uploads. Category matters more than raw subscriber count when estimating revenue: a education channel attracts a specific set of advertisers, and CPMs vary widely between verticals such as finance, software, education, gaming and entertainment. The channel lists CA as its primary country, which further shapes its CPM because advertiser spend differs sharply between geographies. Use the Branzly ad revenue, sponsorship and financial freedom calculators to model how a channel like Predictive History converts views into monthly income, sponsorship pricing and long-term wealth.

Every figure on this page comes from the public YouTube Data API, refreshed each time Predictive History is searched on Branzly. Subscriber counts, view counts and video counts are reported directly by YouTube. Monthly views are an estimate: YouTube does not publish per-month analytics for channels you do not own, so Branzly reconstructs recent performance from the views of the channel's latest uploads, falling back to lifetime views divided by channel age when recent data is sparse. Revenue ranges are modelled estimates, not disclosed earnings, and actual payouts depend on monetisation status, watch time, ad formats, seasonality, audience geography and YouTube's revenue share. Nothing on this page is financial advice, and Branzly is not affiliated with or endorsed by Predictive History or YouTube.

Education channels like Predictive History sit in the Education RPM bracket. Advertisers pay more for audiences that buy — finance, business and tech pay the highest rates, while entertainment, gaming and music rely on huge view volume. Seasonality matters too: Q4 ad rates commonly run 30–50% higher than January.

Every figure on this Predictive History channel earnings report comes from public YouTube Data API statistics — subscribers, lifetime views, video count and recent upload performance — refreshed when the report is opened. Estimates are modelled, not disclosed income, and not financial advice. Want to test your own assumptions? Open the YouTube money calculator or browse brands that already sponsor similar creators in the sponsors directory.