Joby Aviation (JOBY) Operating Expenses (2020 - 2026)
Joby Aviation (JOBY) reported Operating Expenses of $299.52 million for Q2 2026, up 78.4% from $167.88 million a year earlier and up 16.2% from the prior quarter.
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Analysis
Joby Aviation (JOBY) Operating Expenses (2020 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Joby Aviation's Operating Expenses came in at $999.2 million, up 56.7% year-over-year; for FY2025, it was $773.02 million, up 29.5% from FY2024.
- Operating Expenses has increased for five consecutive years, with a five-year compound annual growth rate of 42.4% (FY2020 to FY2025).
- By year, Operating Expenses came in at $596.89 million in FY2024 (+26.2%), $473.13 million in FY2023 (+20.6%), $392.2 million in FY2022 (+51.4%) and $259.09 million in FY2021 (+95.9%).
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses in data going back to Q3 2020.
- Year over year, Operating Expenses has now increased in each of the last 20 quarters, with growth averaging 36.5% over the last eight quarters.
- Across the past five years, year-over-year growth in Operating Expenses ran from 5.7% in Q1 2023 to 105.9% in Q1 2022.
- Per Business Quant data, the three quarters before Q2 2026 came in at $257.83 million (Q1 2026), $237.61 million (Q4 2025) and $204.24 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | General Electric | 324.09 Bn | 278.09 Bn | 4.68 Bn | 10.86 Bn |
| 2 | Rtx | 250.22 Bn | 223.44 Bn | 5.13 Bn | 21.96 Bn |
| 3 | Boeing | 147.08 Bn | 53.78 Bn | 2.41 Bn | 2.35 Bn |
| 4 | Lockheed Martin | 117.51 Bn | 104.24 Bn | 2.45 Bn | - |
| 5 | Howmet Aerospace | 90.46 Bn | 86.07 Bn | 951.00 Mn | 156.00 Mn |
| 6 | General Dynamics | 89.77 Bn | 76.88 Bn | 2.18 Bn | 12.63 Bn |
| 7 | Motorola Solutions | 73.88 Bn | 70.24 Bn | 1.68 Bn | 756.00 Mn |
| 8 | Northrop Grumman | 68.68 Bn | 57.92 Bn | 2.12 Bn | 9.78 Bn |
| 9 | Honeywell International | 66.86 Bn | 19.30 Bn | 3.65 Bn | 1.87 Bn |
| 10 | Joby Aviation | 5.97 Bn | 5.97 Bn | - | 299.52 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 299.52 Mn |
| Mar 31, 2026 | 257.83 Mn |
| Dec 31, 2025 | 237.61 Mn |
| Sep 30, 2025 | 204.24 Mn |
| Jun 30, 2025 | 167.88 Mn |
| Mar 31, 2025 | 163.28 Mn |
| Dec 31, 2024 | 149.93 Mn |
| Sep 30, 2024 | 156.72 Mn |
| Jun 30, 2024 | 144.32 Mn |
| Mar 31, 2024 | 145.92 Mn |
| Dec 31, 2023 | 129.27 Mn |
| Sep 30, 2023 | 128.17 Mn |
| Jun 30, 2023 | 115.97 Mn |
| Mar 31, 2023 | 99.72 Mn |
| Dec 31, 2022 | 101.36 Mn |
| Sep 30, 2022 | 97.12 Mn |
| Jun 30, 2022 | 99.38 Mn |
| Mar 31, 2022 | 94.34 Mn |
| Dec 31, 2021 | 77.19 Mn |
| Sep 30, 2021 | 67.70 Mn |
API Access
Joby Aviation Operating Expenses 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=operating-expenses&ticker=JOBY&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "JOBY", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=JOBY&period=max&api_key=YOUR_API_KEY");
const data = await res.json();