Applied Energetics (AERG) Operating Expenses (2011 - 2026)
Applied Energetics' Operating Expenses was $4.34 million in Q2 2026, up 14.0% from $3.81 million a year earlier and up 13.3% from the prior quarter.
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Analysis
Applied Energetics (AERG) Operating Expenses (2011 - 2026) Analysis & Trends
On a trailing twelve-month basis, Applied Energetics' Operating Expenses was $16.29 million through Jun 30, 2026, up 32.9% year-over-year; for FY2025, it was $15.18 million, up 49.9% from FY2024.
- Operating Expenses has now increased for ten consecutive years, with a five-year compound annual growth rate of 23.6% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $10.12 million in FY2024 (+7.8%), $9.39 million in FY2023 (+38.7%), $6.77 million in FY2022 (+23.1%) and $5.5 million in FY2021 (+4.6%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses in data going back to Q1 2011.
- Compared with a year earlier, Operating Expenses has increased for 15 straight quarters, with growth averaging 30.4% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q3 2023 (growth of 72.7%); the worst was Q3 2022 (a decline of 5.5%).
- Per Business Quant data, AERG's Operating Expenses in the three quarters before Q2 2026 was $3.83 million (Q1 2026), $4.09 million (Q4 2025) and $4.03 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | General Electric | 324.11 Bn | 277.88 Bn | 4.68 Bn | 10.86 Bn |
| 2 | Rtx | 259.48 Bn | 230.96 Bn | 5.13 Bn | 21.96 Bn |
| 3 | Boeing | 151.52 Bn | 58.21 Bn | 2.41 Bn | 2.35 Bn |
| 4 | Lockheed Martin | 126.52 Bn | 113.24 Bn | 2.45 Bn | - |
| 5 | General Dynamics | 92.31 Bn | 79.43 Bn | 2.18 Bn | 12.63 Bn |
| 6 | Howmet Aerospace | 88.22 Bn | 83.82 Bn | 951.00 Mn | 156.00 Mn |
| 7 | Motorola Solutions | 76.09 Bn | 72.44 Bn | 1.68 Bn | 756.00 Mn |
| 8 | Northrop Grumman | 72.54 Bn | 61.78 Bn | 2.12 Bn | 9.78 Bn |
| 9 | Honeywell International | 66.60 Bn | 18.72 Bn | 3.65 Bn | 1.87 Bn |
| 10 | Applied Energetics | 196.56 Mn | 183.54 Mn | 40,430.00 | 4.34 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 4.34 Mn |
| Mar 31, 2026 | 3.83 Mn |
| Dec 31, 2025 | 4.09 Mn |
| Sep 30, 2025 | 4.03 Mn |
| Jun 30, 2025 | 3.81 Mn |
| Mar 31, 2025 | 3.25 Mn |
| Dec 31, 2024 | 2.58 Mn |
| Sep 30, 2024 | 2.61 Mn |
| Jun 30, 2024 | 2.46 Mn |
| Mar 31, 2024 | 2.47 Mn |
| Dec 31, 2023 | 2.52 Mn |
| Sep 30, 2023 | 2.37 Mn |
| Jun 30, 2023 | 2.21 Mn |
| Mar 31, 2023 | 2.28 Mn |
| Dec 31, 2022 | 1.89 Mn |
| Sep 30, 2022 | 1.37 Mn |
| Jun 30, 2022 | 1.73 Mn |
| Mar 31, 2022 | 1.78 Mn |
| Dec 31, 2021 | 1.66 Mn |
| Sep 30, 2021 | 1.45 Mn |
API Access
Applied Energetics 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=AERG&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "AERG", "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=AERG&period=max&api_key=YOUR_API_KEY");
const data = await res.json();