The @nytimes recently published a story about Dan Bishop's investigation into Bexar County's recent Republican primary, and a mysterious file that surfaced during that election.
They attribute the creation of the file to a drag and drop error in Excel--something that is easy to do. In this context, they may as well have said that a train crashing into a Volkswagen creates a Maserati instead of a crushed Volkswagen.
Here's why they're wrong /1
They attribute the creation of the file to a drag and drop error in Excel--something that is easy to do. In this context, they may as well have said that a train crashing into a Volkswagen creates a Maserati instead of a crushed Volkswagen.
Here's why they're wrong /1
A glitch, a crash, a drag-and-drop error — these all share one property: they damage or degrade what's already there. A train hitting a Volkswagen leaves recognizable Volkswagen parts. It does not manufacture a Maserati from the wreckage. Glitches destroy structure. They do not create it. Keep that in mind. /2
Here's the math the NYT didn't mention. The 4,110 suspicious records in the file obtained by @WestonMartinez and @LorionaFarm span a precise range of voter ID numbers. That span, divided by the gap between consecutive records, equals exactly 4,109 — a perfect integer with zero remainder. A drag-and-drop error cannot produce a perfect integer quotient. Only deliberate computation does. /3
The 4,110 fake IDs were placed inside a void in the Texas statewide voter ID space — a gap of 778 million consecutive ID numbers where no Texas voter exists. Not in #BexarCounty. Not anywhere in #Texas. Finding that void required querying all 18.3 million voter records across all 254 Texas counties. A drag-and-drop error does not perform statewide database reconnaissance. /4
At full decimal precision, the gaps between consecutive fake records resolve into exactly four distinct values, cycling in a palindromic pattern that repeats throughout the entire sequence. This is the floating-point fingerprint of a specific algorithm executing in a compiled programming language. It is not the fingerprint of Excel. It is not the fingerprint of a glitch. It is the fingerprint of purpose-written code. /5
The algorithm had to solve a problem: how do you give fake copies of a real voter a believable address? If two real voters share a household, incrementing both of their house numbers creates a collision — two fake records with identical fabricated addresses, which would be a visible anomaly. So the algorithm applied a rule: if your address is unique among the 735 anchor voters, your fake copies get incrementing house numbers (+1, +2, +3). If you share an address with another anchor voter, all your fake copies get the exact same address as the real one — no increment. Two different strategies, one coherent logic. A drag-and-drop error does not make design decisions. /6
The fake records were distributed across precincts in a structured way consistent with the algorithm processing the check-in data precinct by precinct. Every precinct. Every synthetic record. Every structural property consistent across the entire file. This is not what random damage looks like. This is what a specification looks like. /7
One correction for the record: the NYT attributed this analysis to Dr. Walter Daugherity (@ZoomWalter). Dr. Daugherity is a valued collaborator. However, the Substack the Times appears to have been reading is mine. Dr. Daugherity does not have a Substack. /8
The NYT article I am responding to, for those who want to read it directly: [] /endnytimes.com/2026/05/15/us/…
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