Welcome to Blank Metal’s Weekly AI Headlines.
Each week, our team shares the AI stories that caught our attention—the articles, announcements, and insights we’re actually discussing internally. We curate the best of what we’re reading and add the context that matters: what happened, why it matters, and what to do about it.
Short, sharp, and focused on impact.
1. ChatGPT’s New Image Generation is Here
What: OpenAI rolled out its new image generation capabilities directly within ChatGPT, making it significantly easier for users to create and edit images through natural conversation.
So What: This closes the gap between ideation and visual output for enterprise teams—marketing, product, and ops can now prototype visuals, mockups, and diagrams without leaving their AI workflow or waiting on design resources.
Now What: Audit where your teams currently bottleneck on visual assets; this may shift the build-vs-outsource calculus for routine creative work.
Shared by Dan Wick
2. Enterprise AI Theater: A $1.4M Cautionary Tale
What: A viral satirical post mocks enterprise AI adoption theater—depicting a fictional $1.4M Copilot rollout where only 12 of 4,000 employees actually used the tool, while executives got exemptions and made-up metrics fueled glowing case studies.
So What: The satire resonates because it mirrors real patterns: enterprises are spending millions on AI tools without measuring adoption, fabricating ROI narratives, and exempting the leaders who approved the spend from actually using the products.
Now What: Before your next AI tool expansion, audit actual usage data—not projected savings—and require executive sponsors to be active users, not just budget approvers.
Shared by Dan Wick
3. Engineering Is Dead. Long Live “Taste.”
What: A senior engineer argues that coding skill is being displaced by “taste”—the ability to recognize quality output and guide AI tools toward it.
So What: This reframes the AI talent question for enterprise leaders: you may need fewer people who can write code and more who deeply understand what excellent outcomes look like in your domain.
Now What: Audit your technical hiring criteria—pattern recognition and domain expertise may now outweigh raw coding ability.
Shared by Dan Wick
4. Anthropic Winning the AI Talent War
What: SignalFire data reveals Anthropic is winning the AI talent war—engineers leave OpenAI for Anthropic at 8x the reverse rate, and DeepMind at 10.6x.
So What: Talent flow is a leading indicator of where the smartest builders see momentum, and this signals Anthropic’s rising gravitational pull in the race to define enterprise-grade AI.
Now What: When evaluating AI partners or foundation model bets, track where top researchers are moving—not just where they’ve been.
Shared by Dan Wick
5. Huxe: AI-Powered Email Briefings
What: Huxe, an AI-powered email briefing tool, synthesizes personal and work inboxes into audio summaries with prioritized tasks, relevant news commentary, and personalized scheduling suggestions.
So What: This signals growing demand for AI that doesn’t just summarize information but actively triages and contextualizes it—a model enterprise teams should watch as email overload remains a persistent productivity drain.
Now What: If your team struggles with information overload, evaluate whether AI triage tools could reduce time spent on inbox management—but weigh productivity gains against the data access these tools require.
Shared by Elli Rader
6. ChatAgency.ai: Deep Research as a Service
What: ChatAgency.ai is a new product that wraps deep research APIs into a more accessible interface, signaling a growing trend of middleware solutions built on top of AI research capabilities.
So What: Even if individual wrappers aren’t impressive, the pattern matters—expect a wave of startups packaging raw AI APIs into vertical-specific tools, which means enterprises will face more build-vs-buy decisions for specialized research workflows.
Now What: Before evaluating wrapper products, ensure your team understands what’s available directly from foundation model providers—the value gap is often smaller than vendors suggest.
Shared by Eric Johnson
7. OpenAI’s New “Skills” Documentation
What: OpenAI published a “skills” list for ChatGPT that catalogs 300+ specific capabilities across domains like coding, data analysis, writing, and math—providing users a clearer picture of what the model can actually do.
So What: For enterprise teams evaluating AI tools, this kind of transparency helps match capabilities to real use cases instead of relying on marketing claims or trial-and-error discovery.
Now What: Use capability lists like this to audit where your current AI workflows are underutilizing available features—or where gaps remain that require other tools.
Shared by Dan Wick
8. OpenAI Opens ChatGPT to Third-Party Apps
What: OpenAI opened a submission process for developers to have their apps featured directly within ChatGPT’s interface.
So What: This creates a new distribution channel for enterprise AI tools—but also signals OpenAI’s play to become the app store layer for AI, which could reshape build-vs-integrate decisions.
Now What: If you’re building internal AI tools, assess whether ChatGPT distribution aligns with your GTM strategy—or if it creates dependency risk you’d rather avoid.
Shared by Dan Wick
9. AI’s Next Era: The Age of Orchestrators
What: Search Engine Land argues that AI is shifting from standalone tools to “orchestrators” that coordinate multiple AI agents and data sources to complete complex workflows.
So What: For enterprise leaders, this signals that competitive advantage will increasingly come from how well you integrate and orchestrate AI capabilities across your stack—not just which individual models you deploy.
Now What: Audit your current AI tools for interoperability; siloed AI investments today may become integration headaches as orchestration layers mature.
Shared by Eric Johnson
10. Claude Code Skills Marketplace Coming?
What: Speculation is growing that Anthropic may eventually launch a marketplace for Claude Code “skills”—reusable, shareable capabilities that extend what the AI coding assistant can do.
So What: If realized, a skills marketplace could mirror the app store model that made Salesforce and Slack sticky platforms, creating new ecosystem dynamics enterprise teams should watch as they evaluate build-vs-buy for AI tooling.
Now What: If you’re building custom AI workflows in Claude Code, consider whether your solutions could become shareable skills—early ecosystem contributors often capture outsized visibility.
Shared by Dan Wick
Generated with love (and AI) on December 18, 2025
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