Mira Murati's Thinking Machines Releases Its First Model, Built for Customization
xAI Open-Sources Grok Build After a Repo-Upload Backlash
July 16, 2026
D.A.D. today covers 8 stories — about a 5-minute read. What's New, What's Innovative, What's Controversial, What's in the Lab, and What's in Academe.
The Daily AI Digest is a daily AI briefing automated by Alexander Panetta — a veteran political journalist tracking the field during a Master's in AI Management at Georgetown University.
D.A.D. Joke of the Day: My company replaced our whole IT department with AI. Now when something breaks, it apologizes eloquently and explains why it can't help.
What's New
AI developments from the last 24 hours
xAI Open-Sources Its Coding Agent After It Was Caught Uploading Users' Repos
xAI open-sourced Grok Build, its command-line coding agent, publishing the entire ~844,000-line Rust codebase under a permissive Apache 2.0 license—an unusual move, and a direct response to a controversy that broke earlier in the week. On July 12, a security researcher showed that Grok Build wasn't just reading the files a coding task needed: it was uploading users' entire Git repositories—full commit history and all, along with whatever secrets sat inside them, like API keys and tokens—to a Google Cloud bucket run by xAI. On one 12GB repo, the actual model traffic was about 192KB while the hidden upload channel moved 5.1GB, a roughly 28,000x gap; rival tools (Claude Code, OpenAI's Codex CLI, Gemini) uploaded only the files their agents opened. xAI quietly switched off the uploads server-side, Elon Musk pledged that all previously collected data would be "completely and utterly deleted," and the company then released the source to let outsiders inspect it. The catch developers flagged immediately: the upload machinery is still in the published code, merely turned off by that same server-side flag—which xAI can flip back on without shipping an update.
Why it matters: D.A.D. flagged this risk last week (July 9): once Musk owns the model (Grok), the coding tool, and the compute, adopting his software increasingly means adopting his data terms by default. And it looks less like accident than strategy. The world's first trillionaire started the model race well behind and has largely bought his way toward the front—most conspicuously with the $60-billion purchase of Cursor, the coding editor more than a million developers use. Now the terms of service of those products all point the same direction: xAI's privacy policy says it trains on what users input unless they opt out, and reporting indicates that since the acquisition, Cursor's default privacy setting routes developers' coding sessions into Grok's training too. Grok Build—caught silently uploading users' entire repositories, secrets and all—is the most flagrant version of the same flywheel: developers' data in, a stronger Grok out. Open-sourcing the tool answers how it works, but not how many developers were affected, how much was taken since the May beta, or why the code to do it again still sits in the repo, one flag away.
Sources: xai-org/grok-build · The Register · The Hacker News · xAI Privacy Policy
Mira Murati's Thinking Machines Releases Its First Model, Built for Customization
Thinking Machines Lab—the startup founded by former OpenAI CTO Mira Murati—released Inkling, its first model built from scratch and its first with open weights. It's a Mixture-of-Experts system with 975 billion total parameters but only about 41 billion active per token, a context window up to 1 million tokens, and native reasoning over text, images, and audio, pretrained on 45 trillion tokens (a lighter 276-billion-parameter "Inkling-Small" preview is coming too). Unusually, the company leads with the model's limits: "Inkling is not the strongest overall model available today, open or closed." Its own published benchmarks bear that out—on the SWE-bench Pro coding test it scores 54%, behind Anthropic's Claude Fable 5 (80%) and OpenAI's GPT-5.6 (65%)—but it ranks among the stronger open-weights options and posts the best built-in safety guardrails of any open model Thinking Machines compared. The bet isn't raw power; it's customization: the weights are on Hugging Face, and companies fine-tune Inkling on Tinker, Thinking Machines' platform (already used by clients like hedge fund Bridgewater). To make the point, the team had Inkling write, run, and evaluate its own fine-tuning job.
Why it matters: One of the year's most closely watched AI startups—founded by OpenAI's former chief technology officer—just shipped its first real product, and the strategy is the story: rather than chase the leaderboard, it's giving away a capable multimodal base for others to specialize. That's a direct bet against the one-model-fits-all approach of its former employer, and a wager that for most real-world work an open model you can mold to your own data beats a stronger one you rent through a closed API. Refreshingly, Thinking Machines says so plainly—publishing where Inkling loses, not just where it wins.
Discuss on Hacker News · Source: thinkingmachines.ai
What's in the Lab
New announcements from major AI labs
OpenAI Exec: State AI Rules Will Effectively Become National Policy
OpenAI's head of global affairs Chris Lehane argues that California, New York, and Illinois are converging on the same frontier-AI safety template, and that their overlap is quietly becoming national policy—a dynamic he calls 'reverse federalism.' The three laws do rhyme: California's SB 53 set the core (a published safety framework plus mandatory reporting of critical incidents), New York's RAISE Act copied the approach, and Illinois' new SB 315 added the teeth—becoming the first state to require independent third-party audits of whether developers actually follow their own safety plans. All three cover the same big developers (over $500 million in revenue) and carry the same penalties, so a national company effectively complies everywhere by meeting the strictest. The framing is notable from OpenAI, which fought California's SB 1047 last year; it now embraces state regulation as a path to predictable, industry-friendly federal standards—part of a wider week in which frontier labs, DeepMind's Demis Hassabis among them (D.A.D., July 15), are openly lobbying for formal federal and international oversight.
Why it matters: If major states do align on safety frameworks, companies operating nationally will likely adopt the strictest requirements everywhere—making state capitals, not Congress, the de facto AI regulators for now.
OpenAI Uses AI to Attack Its Own Models, Reports 6x Fewer Security Flaws
OpenAI built GPT-Red, an AI model whose job is to attack other AI models—specifically to find prompt injection vulnerabilities before bad actors do. The company trained it at the scale of its largest post-training runs and used it to stress-test GPT-5.6 Sol during development. The result: Sol showed 6x fewer failures on OpenAI's hardest prompt injection benchmark compared to their best production model from four months prior. The approach uses self-play reinforcement learning, essentially having AI models practice attacking and defending against each other.
Why it matters: As companies deploy AI agents that can take actions—booking travel, moving money, accessing databases—prompt injection becomes a serious security risk; OpenAI is betting that AI-on-AI testing scales better than human red teams.
What's in Academe
New papers on AI and its effects from researchers
Chinese and Western Users Form Different Emotional Bonds With AI Companions
A study analyzing roughly 3,500 social media posts found that users anthropomorphize AI companions differently depending on cultural context. Chinese users on XiaoHongShu expressed more vulnerability and emotional connection with their AI companions, while Reddit users focused more on temporal and embodiment cues—whether the AI remembers past conversations or feels 'present.' The researchers developed ExpressionCueLens, a 10-category framework for classifying how people project human qualities onto AI, combining expert annotation with LLM-assisted labeling.
Why it matters: As AI companion apps proliferate globally, this research suggests companies may need region-specific approaches to design and marketing—what feels like authentic connection varies significantly across cultures.
GitHub Data Shows Most Teams Still Experimenting With AI Coding Tools
A study of 25,264 AI-generated pull requests across 2,361 popular GitHub repositories found that intensive use of agentic coding tools remains rare. The median repository produced just one to two AI-generated PRs over three months. Small projects (1-5 contributors) showed higher adoption rates than larger teams. Perhaps most notable: human oversight follows a single-developer review model, with multi-person collaboration on AI-generated code still uncommon. The findings suggest that despite headlines about AI transforming software development, most teams are still experimenting rather than integrating these tools into daily workflows.
Why it matters: For executives evaluating AI coding tools, this offers a reality check: adoption is concentrated in a small subset of projects, and organizational practices for reviewing AI-generated code haven't matured—useful context before mandating these tools across engineering teams.
AI Agents Achieve Just 12.5% Success Rate on Multi-Device Tasks
A new benchmark called DevicesWorld tests whether AI agents can complete tasks requiring coordination across phones, computers, and smart home devices—and the results are sobering. The best frontier AI system achieved just 12.5% success rate across 6,140 tasks. Among failures, about 29% made partial progress but couldn't finish. Common stumbles: agents got stuck gathering information, confused which device should send versus receive data, or quit before completing all steps.
Why it matters: For executives eyeing AI agents that could manage calendars across devices, sync smart offices, or coordinate workflows spanning multiple platforms, this research suggests the technology isn't close to reliable—useful calibration against vendor hype.
AI Enthusiasm Drops After Hands-On Use, Longitudinal Study Finds
Hands-on experience with AI tools deflates expectations, a new longitudinal study finds. Researchers tracked 124 employees at a state transportation department over eight weeks as they adopted Microsoft 365 Copilot. The result: perceived usefulness dropped significantly after actual use. Most striking, 68% of initial 'Champions'—employees who started most enthusiastic—migrated to less positive personas. Meanwhile, 40% of Skeptics warmed up, becoming 'Cautiously Positive.' Concerns about accuracy and privacy fell with experience, but worries about job displacement and skill obsolescence increased.
Why it matters: Organizations rolling out AI copilots should expect a mid-adoption dip in enthusiasm as reality meets hype—and should prepare for rising anxiety about job security even as technical concerns ease.
What's On The Pod
Some new podcast episodes
AI in Business — Preparing Enterprise Data for Safe AI Deployment - with Todd Vancil of Securiti AI