Gemini 3.5 Flash Pinpoints Cause of Linux Boot Delays on ASUS Laptops

Google’s AI model “Gemini 3.5 Flash” has played a key role in identifying the cause of unusual Linux kernel boot delays in a high-end laptop. According to a report from Phoronix, the ASUS ROG Strix G16 G614 laptop, equipped with an AMD Ryzen 9 processor and 32GB of RAM, was experiencing a significant issue where the kernel boot process took approximately 36 seconds. Through a post supported by Gemini 3.5 Flash on the kernel mailing list, it was revealed that the issue stemmed from the device’s firmware.

Problem Overview and Discovery

Despite its high-performance specifications, the laptop in question suffered from an abnormal 30-second-plus delay during kernel boot. For most modern laptops equipped with Intel or AMD processors, such lengthy boot times are rare. A user, Marco Scardovi, took the initiative to investigate the issue. During his investigation, he utilized the “Google Antigravity” feature of Google’s Gemini 3.5 Flash model for support.

According to the Phoronix article, the user reported the issue to the kernel mailing list with assistance from Gemini. It was determined that the boot delay was caused by the ASUS firmware keeping the GPIO line for the touchpad asserted (logical Low) during startup.

Technical Cause and Mechanism

Scardovi explained that in these laptops, the firmware keeps the ActiveBoth GPIO line of the touchpad asserted during the start-up process. Within the kernel’s initialization logic, if an ActiveBoth interrupt is detected as Low, the interrupt is replayed once to synchronize the initial state. This process is designed to call the handler synchronously within the probe path. However, in the affected laptops, the interrupt handler either delayed or hung, causing the synchronous call to block for approximately 36 seconds and stalling the entire boot process.

In essence, the issue arises because the hardware initialization stage, which should normally complete almost instantaneously, was significantly delayed by improper state settings in the firmware.

Role of Gemini 3.5 Flash

While Gemini 3.5 Flash contributed to identifying the problem, it did not fully pinpoint the exact root cause. In the kernel mailing list’s code review, AI had initially suggested a connection to the touchpad, but further analysis of the ACPI dump indicated that the GPIO issue might be linked to graphics-related components.

This distinction is critical. While AI successfully helped narrow down the issue to the GPIO-related area, human developers had to conduct additional investigations to uncover the precise root cause. The initial analysis provided by Gemini 3.5 Flash was deemed “partially correct,” and the problem’s resolution ultimately required human expertise.

Proposed Kernel Patch and Future Measures

Currently, a DMI quirk patch has been proposed for the Linux kernel. This patch is an interim measure to bypass the problematic firmware behavior in the ASUS ROG Strix G16 G614 laptop and reduce the prolonged boot time. Once the patch is applied, the kernel boot time is expected to align with the standard for laptops with similar specifications.

However, a permanent solution will require ASUS and AMD to implement appropriate firmware fixes. Discussions are ongoing on the kernel mailing list, urging the vendors to provide firmware updates. Meanwhile, the proposed patch is likely to be integrated into the upstream kernel as a workaround for environments where BIOS updates are unavailable.

Similar Past Cases

This incident is a textbook example of boot delay issues caused by device firmware. In recent years, even high-end laptops have increasingly encountered Linux boot delays due to firmware quality issues. In particular, areas like power-saving features, touchpads, and GPIO control for graphics are prone to problems, as implementations vary across manufacturers.

The Linux kernel development community has responded to such vendor-dependent firmware issues by accumulating workarounds like DMI quirks. However, the fundamental solution lies in improving firmware quality on the vendors’ side. This point was also highlighted in a previous report by this site, “Microsoft Defender’s Privilege Escalation Vulnerability ‘RoguePlanet’ Revealed (https://singulism.com/en/microsoft-defender-rogueplanet-zero-day),” which noted the increasing prevalence of firmware-level issues affecting OS and application performance.

Current State of AI-Assisted Debugging

The support provided by Gemini 3.5 Flash represents a noteworthy example of AI application in kernel debugging. As an AI model trained on a vast codebase and historical discussions, it can assist human developers by identifying problem areas and proposing hypotheses. In this case, the AI helped focus the investigation on the GPIO issue, potentially streamlining the debugging process.

However, there are instances, like this one, where the AI’s analysis is not entirely accurate. This underscores the reality that AI-assisted debugging should be regarded as a “powerful hypothesis generation tool,” while the final validation and fixes remain the domain of human experts. Unlike time-series forecasting models such as Google’s “TimesFM 2.5 (https://singulism.com/en/google-timesfm-2-5-time-series-forecasting),” debugging applications require more advanced causal reasoning and contextual understanding.

Editorial Opinion

This case exemplifies how AI-supported kernel debugging has entered a practical phase. In the short term, models like Gemini 3.5 Flash are likely to increasingly shorten the time developers spend isolating issues. Especially in text-based discussions, such as those on kernel mailing lists, AI-driven initial analysis can significantly enhance efficiency. However, the fact that AI’s conclusions are not always fully accurate highlights the continuing need for human review.

Looking ahead, the future suggests a more integrated role for AI in the kernel development workflow. Within the next one to three years, AI may even be utilized to validate the quality of firmware provided by vendors. While current efforts remain reactive, there is potential for AI to predict and detect inconsistencies in low-level processes like boot initialization during the firmware design phase, leading to smoother hardware-software integration. The ASUS case serves as a key milestone in this transitional phase.

References

• Phoronix — Published on 2026-06-21