Global AI (GLAI) Change in Accured Expenses (2010 - 2026)
Global AI (GLAI) reported Change in Accured Expenses of $384,829 for Q2 2026, up 7.1% from $359,454 a year earlier and up 165.9% from the prior quarter.
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Analysis
Global AI (GLAI) Change in Accured Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Global AI's Change in Accured Expenses came in at $533,357, down 36.9% year-over-year; for FY2025, it came in at $625,252, up 533.2% from FY2024.
- By year, Change in Accured Expenses came in at $98,751 in FY2024 (-57.2%), $230,791 in FY2023, $1,358 in FY2022 (-20.6%) and $1,711 in FY2021.
- Five-year quarterly Change in Accured Expenses spans a low of -$457,925 in Q3 2025 and a high of $461,714 in Q4 2025.
- Year over year, Change in Accured Expenses gained in three of the last four quarters, with growth averaging 14.7%.
- The high point for year-over-year Change in Accured Expenses in five years was Q3 2023 (growth of 409.3%); the low point was Q1 2026 (a decline of 44.8%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $144,739 (Q1 2026), $461,714 (Q4 2025) and -$457,925 (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 284.36 Bn | 269.43 Bn | 2.30 Bn | 369.00 Mn |
| 2 | CrowdStrike Holdings | 258.84 Bn | 239.24 Bn | 1.10 Bn | 72.38 Mn |
| 3 | Fortinet | 130.69 Bn | 116.62 Bn | 1.64 Bn | 75.10 Mn |
| 4 | Snowflake | 112.38 Bn | 99.70 Bn | 1.04 Bn | 108.85 Mn |
| 5 | Datadog | 96.78 Bn | 78.42 Bn | 881.34 Mn | 6.98 Mn |
| 6 | Zscaler | 40.57 Bn | 26.69 Bn | - | - |
| 7 | Okta | 36.94 Bn | 27.04 Bn | 641.00 Mn | 38.00 Mn |
| 8 | Axon Enterprise | 33.75 Bn | 28.28 Bn | 546.45 Mn | 256.58 Mn |
| 9 | MongoDB | 29.88 Bn | 20.35 Bn | 569.77 Mn | 43.09 Mn |
| 10 | Global AI | 116.17 Mn | 115.92 Mn | 82,446.00 | 384,829.00 |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 384,829.00 |
| Mar 31, 2026 | 144,739.00 |
| Dec 31, 2025 | 461,714.00 |
| Sep 30, 2025 | -457,925.00 |
| Jun 30, 2025 | 359,454.00 |
| Mar 31, 2025 | 262,009.00 |
| Dec 31, 2024 | 247,399.00 |
| Sep 30, 2024 | -23,824.00 |
| Jun 30, 2024 | -98,771.00 |
| Mar 31, 2024 | -26,053.00 |
| Dec 31, 2023 | 225,113.00 |
| Jun 30, 2023 | 3,775.00 |
| Mar 31, 2023 | 1,903.00 |
| Dec 31, 2022 | 962.00 |
| Sep 30, 2022 | 962.00 |
| Jun 30, 2022 | 926.00 |
| Mar 31, 2022 | 915.00 |
| Dec 31, 2021 | -1,445.00 |
| Sep 30, 2021 | 935.00 |
| Jun 30, 2021 | 925.00 |
API Access
Global AI Change in Accured 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=change-in-accured-expenses&ticker=GLAI&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "ticker": "GLAI", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=change-in-accured-expenses&ticker=GLAI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();