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Last Tuesday, OpenAI launched ChatGPT for Teens, a dedicated experience featuring enhanced safety safeguards like activity break nudges, custom usage hours, and age-appropriate content filters. The rollout comes amid heightened scrutiny and several lawsuits alleging that interaction with the chatbot contributed to harm or tragic incidents involving young users. Users identified or predicted to be under 18 are automatically enrolled in these protective settings to help foster a safer environment.

A state judge allegedly handed off her entire decision-making process to artificial intelligence, reportedly issuing a court ruling without using any human judgment. One of the attorneys affected by the ruling sued the judge, arguing that outsourcing her job to AI meant the ruling was invalid. However, just over a week ago, a federal court dismissed the lawsuit, ruling that “judicial immunity” protects judges from being sued for money over official court decisions, even if they make major mistakes or use unauthorized methods like AI.

Independent music publisher Round Hill Music, which owns rights to Bonnie Tyler’s “Total Eclipse of the Heart” and Louie Armstrong’s “What a Wonderful World,” among other bangers, has filed a federal lawsuit in California against AI companies Suno and Anthropic, alleging copyright infringement. The publisher claims the companies illegally scraped thousands of songs to train their artificial intelligence models without permission or compensation. Seeking up to $1 billion in damages, Round Hill Music is demanding a jury trial to hold the tech firms accountable.

Lean TECHniques works alongside Midwest companies to leverage technology for business impact. Through advisory, coaching, and embedded delivery, Lean TECHniques helps organizations develop high-performing teams, create environments that fuel innovation, and deliver best-in-class customer experiences. Core areas of expertise include software, cloud, artificial intelligence, product management, and technology leadership.

Adobe just rolled its audio generation tools out to everyone in Firefly – original music, voiceovers and sound effects, all in one place. The part that matters isn't that it makes audio. Plenty of tools do that. It's that everything Firefly generates is cleared for commercial use, so you're not guessing about rights when you drop a track under a client video or a class project. If you've been avoiding AI music because the licensing felt murky, this is the one to try.

Need an image with a transparent background? Just ask ChatGPT’s image model to make a PNG with a transparent background, like we did here (shown with a second version where we added a background color in Photoshop).

Google is once again giving US college students a free year of Google AI Pro – normally $19.99 a month – which includes 4x higher usage limits in Gemini, 5 TB of storage, and Gemini inside Gmail and Docs. Students also get study tools: notebooks with diagnostic quizzes, interactive visualizations and 3D models, and Deep Research running in the background while you work on something else. Students can sign up at one.google.com/ai-student, verification runs through SheerID, you'll need a payment method on file, and the deadline is December 31, 2026.

Large Language Model (LLM): The thing you're actually talking to when you use ChatGPT, Claude, or Gemini. It's a transformer trained on enormous amounts of text — books, websites, code, transcripts — with one deceptively simple job: predict the next chunk of text. That's it. Type "the capital of France is" and it predicts "Paris," not because it looked it up, but because that pattern showed up constantly in what it read. The "large" part is doing a lot of work here: these models have hundreds of billions of parameters, the internal dials that get tuned during training, and they've read more text than a person could get through in a thousand lifetimes.

The surprising part is what falls out of that. Nobody programmed an LLM to write a haiku, debug Python, or explain a contract clause. Those abilities emerged from prediction at scale. Which is also why LLMs fail in the weird ways they do. A model that’s optimizing for plausible-sounding text will happily produce a plausible-sounding citation that doesn't exist. It isn't lying to you and it isn't consulting a database. It's finishing the pattern. Understanding that one thing explains most of what LLMs are great at and most of what they get wrong.