Meta ordered to pay $25 billion penalty in New Mexico Cambridge Analytica case — SkimNews
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- Meta was ordered to pay $25 billion in penalties on Wednesday by a New Mexico court in a data privacy case stemming from the Cambridge Analytica scandal
- A Santa Fe jury found Meta liable in September for more than 43 million violations of the state's Unfair Practices Act, each carrying a maximum $5,000 civil penalty; the jury initially awarded $219.5 billion before Judge Francis Mathew reduced it to the statutory maximum of $25 billion
- Judge Mathew justified the maximum penalty citing "the extraordinary number of willful violations," involvement of "senior Facebook leadership," and "the clear intentional and systematic nature of Facebook's repeated misstatements," adding that Facebook's internal investigation was "willfully ineffective" due to executive cost pressure
- Meta chief legal officer C.J. Mahoney said the company is "confident" it will prevail on appeal, calling the enforcement "a tax on speech to enforce new rules for what people can say online"
- The penalty carries 15% annual interest until paid in full, and New Mexico was excluded from a separate August settlement in which Meta agreed to pay nearly $17 billion to a coalition of state attorneys general in a federal child safety case
- AG Raúl Torrez — who also led New Mexico's March child safety trial against Meta that resulted in a separate $567 million abatement fund order — said the penalty "demonstrates that those violations carry real consequences," while Meta is appealing both rulings
Why it matters: The $25 billion penalty, accruing 15% annual interest, makes Meta one of the most heavily penalized tech companies in U.S. state-court history and gives New Mexico two separate multi-hundred-million-to-billion-dollar Meta judgments on appeal. The judge's reasoning — that senior leadership involvement and a "willfully ineffective" internal investigation justified the maximum statutory penalty — signals courts are willing to escalate when companies fail at self-policing data practices.
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