Something quietly significant is happening in AI right now. The technology is no longer just generating text or images — it’s hunting software bugs that human engineers missed for decades, reshaping how much money the world’s biggest companies are willing to spend, and bumping into some thorny contradictions of its own making. Here’s what caught our attention this week.
Anthropic’s Claude Mythos Found a Bug That’s Been Hiding Since 1997
Anthropic launched Project Glasswing, giving select partners — including AWS, Apple, Cisco, Google, JPMorgan Chase, and Microsoft — early access to its most powerful model yet, Claude Mythos Preview, specifically to hunt down critical software vulnerabilities. The results are striking: in just weeks of internal testing, Mythos identified thousands of zero-day vulnerabilities across every major operating system and web browser. Among them was a 27-year-old bug lurking in OpenBSD — a flaw that had survived countless human audits since 1997.
This isn’t just a headline-grabbing demo. It signals a genuine shift in how AI might be used defensively. The same capabilities that worry security researchers (AI-powered hacking) may also become our best tool for finding and patching weaknesses before attackers do.
Google’s Gemini 3.1 Ultra: Two Million Tokens and True Multimodality
Google launched Gemini 3.1 Ultra with a 2-million token context window — enough to reason across entire codebases, lengthy research documents, or hours of video in a single pass. What makes it notable isn’t just the size: Gemini 3.1 Ultra was designed from the ground up to reason across text, images, audio, and video simultaneously, without routing through separate transcription or processing steps. Google also added a sandboxed Code Execution tool, letting the model run and test its own code inline. With Google I/O around the corner, the company is clearly in sprint mode.
Meta Is Spending Like There’s No Tomorrow
Meta announced AI capital expenditures of $115–135 billion for 2026 — nearly double last year’s spending. That’s an extraordinary number, and it reflects just how seriously the company is taking the gap between itself and OpenAI and Google on frontier model development. Infrastructure at this scale means data centers, chips, energy, and talent, all competing for the same limited pool of resources. Whether this investment pays off in model quality is something we’ll be watching closely throughout the year.
The Agentic Paradox: AI Agents Are Getting Expensive
Here’s the contradiction nobody quite expected: as businesses rush to deploy autonomous AI agents, the cost of the frontier models powering them is rising sharply. Cloudflare recently credited AI with eliminating 1,100 roles — even as it posted record revenue — joining a growing list of tech companies linking headcount reductions to automation. But the irony is real: the efficiency gains AI promises can be partially eaten up by the compute costs of running increasingly capable models. The companies that figure out how to deploy agents cost-effectively will have a significant edge.
Colorado Revamps Its Landmark AI Law
On the regulatory front, Colorado overhauled its groundbreaking AI law — one of the first in the U.S. to specifically target AI systems making consequential decisions about jobs, healthcare, education, housing, and credit. The revisions reflect real-world pushback from industry and a desire to make the law more workable without gutting its core protections. It’s a useful case study in what AI regulation looks like when it moves from theory to practice.
What This Moment Feels Like
This week’s stories share a common thread: AI is becoming something that acts in the world, not just assists with it. Models are autonomously finding vulnerabilities, companies are committing generational levels of capital, and the unintended consequences — cost paradoxes, regulatory friction, workforce disruption — are arriving right alongside the breakthroughs. None of this is reason for panic or uncritical excitement. But it’s worth paying attention, because the decisions being made right now — by companies, regulators, and researchers — will shape how this all unfolds. As always, we’re watching with curiosity.