Faraday Future Intelligent Electric (FFAI) Total Non-Current Liabilities (2020 - 2026)
Faraday Future Intelligent Electric's Total Non-Current Liabilities came in at $305.84 million for Q2 2026, down 9.7% from $338.77 million a year earlier.
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Analysis
Faraday Future Intelligent Electric (FFAI) Total Non-Current Liabilities (2020 - 2026) Analysis & Trends
At the end of FY2025, Faraday Future Intelligent Electric's Total Non-Current Liabilities was $303.16 million, down 1.9% from FY2024.
- Total Non-Current Liabilities carries a five-year compound annual growth rate of -19.5% (FY2020 to FY2025).
- Going back by year, Total Non-Current Liabilities was $309.15 million in FY2024 (+2.7%), $300.97 million in FY2023 (-5.6%), $318.87 million in FY2022 (-5.1%) and $336.06 million in FY2021 (-62.4%).
- The five-year range for quarterly Total Non-Current Liabilities is $249.41 million (Q3 2022) to $391.59 million (Q3 2025).
- Year-over-year, Total Non-Current Liabilities increased in three of the last seven quarters, with growth averaging 3.0%.
- The fastest year-over-year change in Total Non-Current Liabilities over five years came in Q3 2025 (growth of 34.6%), and the weakest in Q1 2022 (a decline of 65.6%).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Tesla | 1,233.56 Bn | 1,059.58 Bn | 4.75 Bn | 47.48 Bn |
| 2 | Ferrari | 70.78 Bn | 63.27 Bn | 1.18 Bn | 5.53 Bn |
| 3 | General Motors | 67.08 Bn | -37.53 Bn | 9.28 Bn | 118.34 Bn |
| 4 | Ford Motor | 47.62 Bn | -94.87 Bn | 6.08 Bn | 219.21 Bn |
| 5 | Honda Motor | 45.97 Bn | -103.84 Bn | 8.46 Bn | 5.28 Bn |
| 6 | Toyota Motor | 24.76 Bn | -376.05 Bn | 20.95 Bn | 3.36 Bn |
| 7 | Magna International | 18.13 Bn | 12.16 Bn | 1.61 Bn | - |
| 8 | Rivian Automotive | 16.20 Bn | -7.11 Bn | 179.00 Mn | 8.76 Bn |
| 9 | Stellantis | 12.29 Bn | -152.36 Bn | 5.55 Bn | 73.67 Bn |
| 10 | Faraday Future Intelligent Electric | 1.32 Mn | -120.94 Mn | -10.70 Mn | 305.84 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 305.84 Mn |
| Dec 31, 2025 | 303.16 Mn |
| Sep 30, 2025 | 391.59 Mn |
| Jun 30, 2025 | 338.77 Mn |
| Mar 31, 2025 | 269.70 Mn |
| Dec 31, 2024 | 309.15 Mn |
| Sep 30, 2024 | 290.90 Mn |
| Jun 30, 2024 | 307.80 Mn |
| Mar 31, 2024 | 297.08 Mn |
| Dec 31, 2023 | 300.97 Mn |
| Sep 30, 2023 | 306.94 Mn |
| Jun 30, 2023 | 279.74 Mn |
| Mar 31, 2023 | 310.40 Mn |
| Dec 31, 2022 | 318.87 Mn |
| Sep 30, 2022 | 249.41 Mn |
| Jun 30, 2022 | 316.13 Mn |
| Mar 31, 2022 | 299.96 Mn |
| Dec 31, 2021 | 336.06 Mn |
| Sep 30, 2021 | 373.81 Mn |
| Jun 30, 2021 | 776.07 Mn |
API Access
Faraday Future Intelligent Electric Total Non-Current Liabilities API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=total-non-current-liabilities&ticker=FFAI&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-current-liabilities", "ticker": "FFAI", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=total-non-current-liabilities&ticker=FFAI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();