Five stories from overseas AI news on October 31 (US time). The centerpiece is a pair of same-day stories about where AI money is flowing. Microsoft has begun offering a voluntary severance program to about 60,000 employees — roughly a quarter of its workforce — while OpenAI was reported to have raised only about $11 billion of its $30 billion funding target. Keep labor costs down, pour enormous sums into infrastructure, and watch investors grow more selective — that is the picture these same-day stories paint together. We also cover the AI-abusing malware "Volta-Autodoxer," Google DeepMind's global forest map "ForTy," and Microsoft's 4B agent model "Mage-VL-Cowork."
Microsoft Offers Voluntary Severance to About 60,000 Employees — AI Infrastructure at a $170 Billion-a-Year Scale
According to [a report from The Decoder](https://the-decoder.com/microsoft-offers-a-quarter-of-its-workforce-voluntary-severance-while-shielding-ai-critical-teams/), Microsoft has begun offering voluntary severance packages to about 60,000 employees — roughly a quarter of its workforce. This is the second round this year, and teams deemed critical to AI are said to be shielded from the program.
What stands out is the contrast in the numbers. The company's headcount has held nearly flat at about 223,000 since 2022, yet it plans to spend roughly $170 billion on AI infrastructure in 2026 alone. The figures reveal a management decision to accelerate infrastructure investment while slimming the workforce through voluntary severance rather than adding headcount.
Why it matters: The view that "AI investment creates jobs" and the fear that "AI destroys jobs" have long coexisted, and here both live inside one company's decisions within the same period. This is voluntary severance that employees choose for themselves, with AI-related teams protected — a conditional arrangement that cannot be equated with across-the-board layoffs. Even so, the fact that Microsoft rolled out $170-billion-scale capital investment and openness to 60,000-scale departures at the same time is essential material for anyone thinking about how AI investment relates to employment.
OpenAI's Fundraise Stops at About $11 Billion — Less Than Half of Its $30 Billion Target
The same day brought a follow-up on OpenAI's fundraising. In [our October 23 roundup](https://devdog-lab.com/ja/blog/2026-10-23-ai-news-digest-run2/), we reported that OpenAI was in talks to raise $30 billion at a $1.25 trillion valuation ahead of its IPO. According to [The Decoder's new report](https://the-decoder.com/openai-raises-just-11-billion-of-its-30-billion-target-as-big-investors-lose-their-ai-appetite/), the latest funding round actually brought in about $11 billion — short of half the target. The $1.25 trillion valuation also reportedly fell short of expectations.
The backdrop: amid the recent plunge in tech stocks, large investors are losing their appetite for AI. It is fair to say the fallout from [the slide that wiped a combined roughly $900 billion off big tech's market caps](https://devdog-lab.com/ja/blog/2026-10-31-ai-news-digest/) has now reached the primary fundraising side as well. Meanwhile, rival Anthropic is reported to be moving toward a listing as early as November, and how deteriorating funding conditions will show up as differences in each company's fundraising strategy deserves continued attention.
"Volta-Autodoxer," an Infostealer That Abuses AI
On the security front, [a Wired report](https://www.wired.com/story/kernel-panic-volta-autodoxer-ai-infostealer-malware/) introduced a new infostealer called "Volta-Autodoxer." The malware abuses AI tools to harvest victims' information. Targets reportedly range from login credentials and credit card numbers to Discord sessions and even UberEats receipts.
The "Autodox" in the name appears to be a coined term implying the automation of doxxing — the public exposure of personal information. This month has already seen a string of incidents targeting developers, including ["Black October," in which nine AI development platforms were compromised](https://devdog-lab.com/ja/blog/2026-10-31-ai-news-digest-run2/), and [malicious extensions placed on the Cursor marketplace](https://devdog-lab.com/ja/blog/2026-10-31-ai-news-digest/). This report shows that AI is now being built into attacks on ordinary users as well. When attackers use AI as labor, the burden on defenders structurally grows. See the original Wired article for the details.
Google DeepMind and WRI Release "ForTy," a Global Map of the World's Forests
On the applied side, Google DeepMind and the World Resources Institute (WRI) have released "ForTy," a global-scale forest map built from satellite data and AI. According to [The Decoder's article](https://the-decoder.com/google-deepmind-creates-a-global-map-of-forests-with-satellite-data-and-ai/), ForTy classifies forest and other land into six categories, including natural forests, plantations, and tree crops.
One expected use is tracking deforestation tied to EU regulation. At the same time, its recognition performance has clear limits: planted forests and tree crop areas are identified only about half the time, and plantations just 60% of the time.
Why it matters: Forest monitoring has mostly depended on countries and research institutions analyzing satellite data on their own. If large-scale AI classification can serve as a shared foundation, it could change how regulatory compliance and research are done. The fact that the limitations are disclosed alongside the results is also a model of honest practice worth referencing when evaluating real-world AI applications.
Microsoft's 4B Vision-Language Model "Mage-VL-Cowork" — Running a Desktop Agent on Device
Finally, research from Microsoft. "Mage-VL-Cowork," [released on Hugging Face](https://www.reddit.com/r/LocalLLaMA/comments/1xk6blw/microsoftmagevlcowork_hugging_face/) and making the rounds on r/LocalLLaMA, is an attempt to teach computer use to a small vision-language model in the 4-billion-parameter class. The approach rests on three pillars: evolving the teacher harness in the upstream stage, building training environments with "Mage-CUA-Forge," a workflow-first environment synthesis; and a five-stage curriculum that moves from specialization to integration.
The result: 66.0% on OSWorld-Verified, described as state of the art at the 4B scale. After online reinforcement learning, model calls dropped 13.0% and output tokens dropped 25.4%, confirming gains on the efficiency side as well. The aim is to run an agent that autonomously operates the desktop on device.
That this research — on running agents more cheaply with small models — came from the same Microsoft, on the same day as the voluntary severance news, is suggestive even if coincidental. It is best understood as part of the broader shift of agent execution from large cloud models to small on-device models.