The prediction was conditional: if the U.S. forgave roughly $0.5T of federal student loans (about the first $10,000 per borrower), GDP would get a one‑time ~2.5% boost.

In reality, that specific broad-based cancellation was never implemented. Biden did announce a plan in August 2022 to cancel up to $10,000–$20,000 per eligible borrower, which independent estimates put at up to about $519 billion in cost—very close to the ~$0.5T scale Chamath referenced—but the U.S. Supreme Court struck this plan down in Biden v. Nebraska in June 2023, so it never took effect.【3view1】【0search7】

Instead, the government pursued a patchwork of targeted relief (PSLF fixes, borrower‑defense discharges, income‑driven repayment corrections, etc.). By early 2025, total forgiveness under these narrower programs was around $180–$190 billion for roughly 5 million borrowers—far below $0.5T—and it was rolled out gradually rather than as a single, discrete shock.【0search6】【0news15】【0search4】 Because the predicted policy was not carried out at the scale or in the one‑time fashion assumed, there is no clean real‑world episode to test whether GDP actually jumped by 2.5% as claimed.

Moreover, macroeconomic modeling available after (and even before) the podcast suggests that large-scale student loan cancellation is a relatively weak stimulus. The Committee for a Responsible Federal Budget estimated that canceling all student loans (about $1.5T) would only boost annual consumer spending by roughly $100 billion—on the order of a few tenths of a percent of GDP per year—characterizing student‑debt cancellation as “poor economic stimulus.”【3view1】 This theoretical work implies that a $0.5T cancellation is unlikely to generate a 2.5% one‑time GDP surge, but that remains a model-based inference, not an observed outcome.

Because the triggering policy never occurred at the predicted scale, and GDP effects cannot be isolated from other shocks, there is no direct empirical test of Chamath’s 2.5% one‑time GDP boost claim. The best we can say is that the prediction’s truth or falsehood cannot be determined from actual outcomes, even though subsequent research suggests it was probably overstated.