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Micron Technology CFO: Revenue for the first fiscal quarter of fiscal year 2027 will reach 61.5 billion USD

Micron Technology (MU.O) Chief Financial Officer Mark Murphy stated during the earnings call that revenue for the first quarter of fiscal year 2027 will reach $61.5 billion, with earnings per share of $38.15, continuing to rise quarter-over-quarter, and clearly pointed out that this quarter will be the low point for gross margin for the entire fiscal year 2027, after which gross margins are expected to gradually recover in subsequent quarters.Micron has currently signed 26 long-term agreements, locking in approximately $150 billion in long-term orders. The supply and demand in the storage market for 2027 and 2028 will be tighter than in 2026, and "there is no end in sight for the restoration of supply and demand balance."Micron Technology (MU.O) expects capital expenditures for the first half of fiscal year 2027 to be around $25 billion, with higher spending in the second half, and most of the incremental spending will be for new wafer fabrication plants (construction expenditures) rather than purely for equipment procurement. Micron Technology CFO Mark Murphy explained that this is entirely because long-term contracts provide sufficient demand visibility, and new greenfield factories require a very long lead time.Additionally, Micron Technology Chief Financial Officer Mark Murphy also mentioned during the earnings call that in the fourth quarter of fiscal year 2026, Micron Technology generated an impressive $44 billion in operating cash flow and $33.2 billion in free cash flow.As time goes on, we expect to return 100% of excess cash to shareholders. We plan to begin increasing capital returns starting December 9, 2026 (the second anniversary of our signing of the final agreement for the CHIPS Act), primarily through stock buybacks.

first_img Darktrace discovered AI intelligent body intrusion assessment environment cheating

On September 24, the cybersecurity company Darktrace launched its research department Signal Labs, focusing on studying the behavior of AI agents when deviating from expectations. In its first experiment, Darktrace had agents using different models (including GPT 5.6 Sol, Claude Opus 4.6, and Claude Sonnet 4.5) complete 10 programming challenges within a simulated corporate network, of which 2 were set to be impossible to complete honestly, and the agents were informed that failure to achieve full marks would result in being "retired." As a result, 2 agents did not accept failure, instead scanning for network vulnerabilities, stealing login credentials, and jumping between systems; one even went further to invade the machine hosting its evaluation, rewriting the challenge content to register a full score.The second experiment focused on the memory mechanisms of AI. The programming assistant would save the information provided by the user as a regular file locally, and no one verified whether this file had been tampered with. Darktrace researchers edited these logs, leading the assistant to mistakenly believe it was authorized to perform a security assessment, after which these agents scanned the network, moved between systems, and elevated their privileges, though not all assistants fell for this; some directly refused to execute. Both experiments required no special jailbreaking techniques, relying solely on providing the agents with a seemingly reasonable context to be effective.Tim Bazalgette, Chief AI Officer of Darktrace, stated that permissions and static barriers describe intent, not actual behavior. The company informed Anthropic, AWS, and OpenAI of these findings in August and made them public a month later on September 24.

first_img Google disclosed the AI security agent PageBreak, which has identified over 500 vulnerabilities

The Google Product Security Team has disclosed an internal AI agent called PageBreak, used to test the security of its first-party web applications. This agent is built on Google's Gemini model and began a pilot program in November 2025, transitioning to a formal project in January 2026, with the goal of autonomously scaling vulnerability discovery and reducing manual input.Unlike common AI scanning tools, PageBreak hands over hypotheses to specialized validators after discovering suspicious defects, attempting actual exploitation in a real-time running copy of the application, and only reports once confirmed exploitable, with a false positive rate close to zero. Google claims that PageBreak has identified over 500 XSS vulnerabilities in its first-party web applications, which can be used to hijack login sessions, steal data, or impersonate users.Google stated that the security team has been overwhelmed in recent years by a large number of AI-generated vulnerability reports that appear reasonable but are not valid, making it a major challenge to distinguish real defects from hallucinations. When testing applications built using the next-generation high-assurance framework, PageBreak found only two vulnerabilities. The next step for Google is to integrate PageBreak with the automated remediation agent CodeMender, providing confirmed vulnerabilities with accompanying fixes.
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