Apr 2, 2026 · 287K views
Nick Shirley interviews former DOGE staffer Edward Coristine (known as 'Big Balls'), now running engineering for the National Design Studio, about the national debt, government waste, and fraud in programs like Medicare, Medicaid and Social Security. They discuss DOGE findings, improper payments, open-sourcing government data, and how Nick's Minnesota fraud investigations spurred change.
Programs involved: Medicare, Medicaid, Medi-Cal, Social Security, SNAP, SBA COVID loans, Trump Accounts, Trump RX, NATO
Figures below are claims made in the video, shown with the status stated there — this site does not verify them. Disclaimer
National debt held by private investors
“$24.4 trillion in private investors”
Stated by Nick Shirley
Annual federal government spending
“we spend about 6 trillion a year and we make about 4 trillion”
Stated by Edward Coristine
Annual federal government revenue
“the government makes right around $4 trillion a year”
Stated by Edward Coristine
Social Security trust fund holdings
“2.7 trillion in social security trust funds”
Stated by Nick Shirley
Annual interest paid solely to service the national debt
“this $1.2 trillion is not going to anything but servicing debt”
Stated by Nick Shirley
US debt held by Japan
“$1.1 trillion to Japan”
Stated by Nick Shirley
US debt held by the United Kingdom
“$800 billion to the United Kingdom”
Stated by Nick Shirley
US debt held by China
“756 billion to China”
Stated by Nick Shirley
Total estimated federal improper payments claimed as excessive
“we shouldn't be having $200 billion of improper payments”
Stated by Nick Shirley
Waste in Medicaid in some individual states
“in some states there's been over a hundred billions uh in waste just in their medic”
Stated by Nick Shirley
Estimated federal improper payments for Medicare and Medicaid
“for Medicare and Medicaid it's around 100 billion”
Stated by Nick Shirley
Annual federal interest payments on the national debt
“it's well past a trillion dollars a year”
Stated by Edward Coristine · verifier note: amount_usd is wrong: it is set to 1,000,000,000 ($1 billion) but the transcript says interest payments are 'well past a trillion dollars a year' (~$1 trillion, i.e. 1,000,000,000,000).
National debt increase during the roughly two-hour podcast
“the national debt has gone up $810 million”
Stated by Nick Shirley
Luxury hotels in NYC to house illegal migrants, diverting FEMA funds
“$60 million in luxury hotels in New York City to house illegal migrants”
Stated by Nick Shirley
DOGE finding of spending on transgender monkey research
“$33 million for transgender monkey research”
Stated by Nick Shirley
Spending on a Sesame Street style children's TV program in Iraq
“$20 million for a Sesame Street style children's TV program in Iraq”
Stated by Nick Shirley
Spending on tax policy consulting in Liberia
“$17 million for tax policy consulting in Liberia”
Stated by Nick Shirley
Spending on Central American gender consultants
“$9 million in Central American gender consultants”
Stated by Nick Shirley
Spending on promoting tourism in Egypt
“$6 million in promoting tourism in Egypt”
Stated by Nick Shirley
Amount a California storefront with no visible business received per government data
“you can see from the government data set that they received a million dollars”
Stated by Nick Shirley
Debt each newborn baby inherits
“Every newborn baby inherits $116,000 in debt”
Stated by Nick Shirley
Typical student loan debt burden for college students
“they have to fight through $50,000 of student loan debt”
Stated by Nick Shirley
Government contribution to a Trump Account at a child's birth
“It is a $1,000 that you get when your child is born”
Stated by Edward Coristine
All figures are as stated in the video — most are allegations, not adjudicated findings. Every dollar figure links to the timestamp where it is said. Extraction QA: The extraction is highly accurate; nearly all claims are quoted verbatim and correctly attributed. The only substantive error is claim 12's amount_usd, which encodes $1 billion instead of the ~$1 trillion the transcript actually states. Entity handling is good, with ASR-garbled names (Bessent, Walz, Quality Learning Center) reasonably reconstructed.
~ = name reconstructed from garbled auto-captions; verify before quoting.