Applied Optoelectronics (AAOI) Operating Expenses (2012 - 2026)
Applied Optoelectronics (AAOI) posted Operating Expenses of $77.93 million for Q2 2026, up 65.3% from $47.14 million a year earlier and up 36.9% from the prior quarter.
Applied Optoelectronics (AAOI) Operating Expenses (2012 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Applied Optoelectronics was $239.74 million, up 55.4% year-over-year; for FY2025, it was $191.52 million, up 44.3% from FY2024.
- Annual Operating Expenses has increased for three consecutive years, with a five-year compound annual growth rate of 14.0% (FY2020 to FY2025).
- In prior years, Applied Optoelectronics' Operating Expenses was $132.71 million in FY2024 (+32.4%), $100.27 million in FY2023 (+8.3%), $92.63 million in FY2022 (-2.0%) and $94.48 million in FY2021 (-4.9%).
- The Q2 2026 figure stands as the highest quarterly Operating Expenses in data going back to Q3 2012.
- On a year-over-year basis, Operating Expenses has increased in each of the last 15 quarters, with growth averaging 42.8% over the last eight quarters.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q2 2026, with growth of 65.3%; the weakest was Q2 2022, with a decline of 11.9%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $56.91 million (Q1 2026), $53.45 million (Q4 2025) and $51.45 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Apple | 4,943.67 Bn | 4,691.16 Bn | 54.77 Bn | 19.08 Bn |
| 2 | Cisco Systems | 421.20 Bn | 357.13 Bn | 11.06 Bn | 6.80 Bn |
| 3 | Dell Technologies | 347.85 Bn | 303.60 Bn | 9.83 Bn | 4.45 Bn |
| 4 | Arista Networks | 258.42 Bn | 211.87 Bn | 1.91 Bn | 532.30 Mn |
| 5 | Sandisk | 250.08 Bn | 238.60 Bn | 7.58 Bn | 545.00 Mn |
| 6 | Seagate Technology Holdings | 208.99 Bn | 203.98 Bn | 1.90 Bn | 2.07 Bn |
| 7 | Western Digital | 163.62 Bn | 151.74 Bn | 2.03 Bn | 465.00 Mn |
| 8 | Sony | 143.48 Bn | 93.86 Bn | 6.53 Bn | 14.83 Bn |
| 9 | Hewlett Packard Enterprise | 83.23 Bn | 61.11 Bn | 4.93 Bn | 10.82 Bn |
| 10 | Applied Optoelectronics | 8.17 Bn | 6.89 Bn | 53.21 Mn | 77.93 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 77.93 Mn |
| Mar 31, 2026 | 56.91 Mn |
| Dec 31, 2025 | 53.45 Mn |
| Sep 30, 2025 | 51.45 Mn |
| Jun 30, 2025 | 47.14 Mn |
| Mar 31, 2025 | 39.48 Mn |
| Dec 31, 2024 | 35.20 Mn |
| Sep 30, 2024 | 32.46 Mn |
| Jun 30, 2024 | 35.81 Mn |
| Mar 31, 2024 | 29.24 Mn |
| Dec 31, 2023 | 26.14 Mn |
| Sep 30, 2023 | 26.86 Mn |
| Jun 30, 2023 | 23.86 Mn |
| Mar 31, 2023 | 23.41 Mn |
| Dec 31, 2022 | 24.59 Mn |
| Sep 30, 2022 | 23.25 Mn |
| Jun 30, 2022 | 21.53 Mn |
| Mar 31, 2022 | 23.26 Mn |
| Dec 31, 2021 | 21.72 Mn |
| Sep 30, 2021 | 23.58 Mn |
Applied Optoelectronics 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=AAOI&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "AAOI", "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=AAOI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();