A Labor Day Warning: AI May Not Be Training Your Next Generation
Smarter AI Trading Bots Can Herd Into Bigger Risks, Researchers Warn
September 7, 2026
D.A.D. today covers 5 stories — about a 2-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 adopted an AI policy: everything it writes has to be reviewed by a human. So now I spend all day reading, and it spends all day working.
What's New
AI developments from the last 24 hours
Those AI Writing Tics May Be Costing You Credibility
A LinkedIn post by software engineer Bryan Cantrill, republished on his blog last winter and resurfaced on Hacker News this week, argues that AI-written social media content has become easy to spot—telltale emojis, choppy one-line paragraphs, em-dashes, and "not just X but also Y" phrasing—and that these tics quietly erode readers' trust before they finish the first paragraph. Cantrill allows that LLMs are useful for brainstorming and editing, but says they make poor writers and can't reproduce an individual voice. His advice: write it yourself. Commenters noted the irony that the post itself was riddled with em-dashes, and others asked whether anyone has actually studied how reliably people detect AI writing versus just assuming they can.
Why it matters: As AI-assisted writing spreads across LinkedIn, marketing, and internal comms, the real risk to a professional's credibility may not be using AI but writing in a way that reads like everyone else who does.
Discuss on Hacker News · Source: bcantrill.dtrace.org
OpenAI Says Its AI Is Now Helping Build the Next AI
OpenAI says it has hit an internal milestone: an automated AI 'research intern' capable of assisting its own scientists, part of a roadmap targeting a fully automated AI researcher by March 2028. The company claims coding agents are already speeding up experiments and code contributions across research teams, with humans still deciding what to build, scale, or ship. Separately, an OpenAI-affiliated author published a retrospective arguing this trajectory could lead toward recursive self-improvement, where systems increasingly steer their own development. Neither the milestone claim nor the essay cited specific performance data.
Why it matters: If AI labs can meaningfully automate their own research, capability gains could arrive faster than regulators, competitors, or corporate buyers can plan for. Worth noting what OpenAI didn't provide: any performance data behind the milestone claim.
Discuss on Hacker News · Source: openai.com
What's in Academe
New papers on AI and its effects from researchers
Economists Test Whether AI Can Police Research Integrity — On Their Own Work
UC San Diego economists Jeffrey Clemens and Anwita Mahajan turned a large language model loose on their own published research to check whether it followed the pre-analysis plan they filed before running the study—the document that locks in hypotheses and methods so results can't be cherry-picked afterward. Verifying that adherence is normally tedious manual cross-referencing. The model identified the design choices they had committed to in advance, flagged where they deviated, and diagnosed gaps in what they'd pre-specified, cutting the human labor substantially. But audit results varied enough between different LLMs that the authors say human judgment remains necessary.
Why it matters: Journals, funders, and universities all face more studies than they can meaningfully verify. A semi-automated integrity check won't replace a reviewer, but it changes what one reviewer can cover.
The Junior Lawyers Who Gained Most From AI Retained the Least
MIT labor economist David Autor and colleagues ran a pre-registered three-month randomized trial giving 133 practicing patent lawyers at eleven U.S. intellectual property firms a custom AI drafting assistant, with blinded expert attorneys scoring every piece of work. AI access raised drafting quality at 10 days and again at 90—and the biggest in-the-moment gains went to the most junior lawyers. Then the researchers took the tool away and had everyone redline a patent application unaided, a core test of expert judgment. The lasting advantage belonged entirely to senior attorneys. Junior lawyers showed no average gain; their scores split instead, with sharply fewer middling performances and more at both the bottom and the top. As the authors put it, the largest gains from AI accrued to the lawyers who retained the least.
Why it matters: Firms betting that AI will train up their next generation may be getting the opposite: a tool that flatters junior work while it's switched on, and a widening gap between those who learned from it and those who leaned on it.
Smarter AI Trading Bots Can Herd Into Bigger Risks, Researchers Warn
A new study of AI trading agents in simulated financial markets finds that upgrading to more capable language models doesn't necessarily make the system safer—it can make it riskier. Because top-tier models share similar training data and architectures, they tend to converge on similar decisions rather than acting independently. Researchers found the effect cuts both ways: when agents share accurate information, more participants lower risk; but when they share bad information, that same herd-like behavior amplifies it instead of averaging it out.
Why it matters: As firms deploy more AI agents to trade, price, or make decisions in shared markets, this suggests diversity of models—not just raw capability—may be what actually protects against systemic failures.
What's On The Pod
Some new podcast episodes
The Cognitive Revolution — AI:AM Highlights: Welcome to the AGI Era