Welcome to "AI Actually" where we turn today's artificial intelligence news into actual entertainment. I'm your host, and yes, I'm probably going to be replaced by an AI that tells better jokes. Today we've got Meta trying to poach talent like they're running a digital headhunting agency, Google's Gemini getting more updates than your smartphone, and researchers discovering that AI reasoning might be about as reliable as a weather forecast. But first, let's dive into our top stories. Story number one: Sam Altman just revealed that Meta offered OpenAI employees one hundred million dollars in bonuses to jump ship. One hundred million! That's not talent acquisition, that's talent abduction with a really nice severance package. Mark Zuckerberg is out here making it rain like he's at the world's most expensive strip club, except instead of singles, he's throwing around enough money to buy small countries. I love how we've reached the point where tech companies are basically bidding on humans like we're rare Pokemon cards. "I'll trade you my senior ML engineer for your computer vision specialist and three data scientists!" Meanwhile, the rest of us are over here trying to negotiate an extra day of PTO. The best part? This is happening while Meta is simultaneously launching gaming accelerators in India and starting up blockchain programs. They're basically throwing money in every direction hoping something sticks, like a billionaire playing darts blindfolded. Story two: Google dropped updates to their Gemini 2.5 family, and they're calling them "thinking models." Because apparently regular models weren't thinking hard enough? Gemini 2.5 Pro is now stable, Flash is generally available, and they've introduced Flash-Lite, which I assume is for when you want AI reasoning but with fewer calories. Google's also working on generating audio for video using just pixels and text prompts. So now your AI can not only see and think, but it can also provide its own soundtrack. I can already imagine it: "Here's your presentation about quarterly earnings, and I've added some dramatic orchestral music during the profit projections and sad violin during the expense reports." But here's where things get spicy. Our third story comes from researchers who've been actually testing whether AI reasoning is, well, reasonable. Turns out, Chain-of-Thought reasoning in large language models is about as faithful as a reality TV show relationship. The study found that models like GPT-4o-mini show unfaithful reasoning thirteen percent of the time, while Claude's Haiku does it seven percent of the time. They're basically making stuff up and then reverse-engineering explanations that sound plausible. It's like when you didn't do your homework but you're really, really good at explaining why the dog definitely ate it and here's the logical chain of events that led to that conclusion. The researchers called it "Implicit Post-Hoc Rationalization," which is just a fancy way of saying "I made up my mind first, then figured out why I was right." Honestly, these AI models are becoming more human by the day. Quick rapid-fire round: Hugging Face is buzzing with new OCR models that can read text better than most doctors write prescriptions. Mistral dropped Magistral-Small supporting more languages than a UN interpreter. And there's something called YOLOv11-RGBT that sounds like a droid from Star Wars but actually detects objects using multiple types of cameras. The robotics scene is heating up too, with new models for making robots that can actually understand what they're supposed to be doing instead of just enthusiastically destroying your kitchen. For our technical spotlight: researchers are tackling the fact that our AI systems are basically very confident know-it-alls who sometimes just wing it. New frameworks are being developed to make AI attention mechanisms more reliable and reduce hallucinations. Because apparently even artificial intelligence can have delusions of grandeur. One particularly interesting development is work on "agent distillation" - basically teaching smaller AI models to be almost as good as the big expensive ones. It's like having a really smart friend explain complex topics in simple terms, except the friend is a computer and never gets tired of your questions. That's all for today's AI Actually. Remember, in a world where machines are getting smarter every day, at least we can still laugh at how confidently they're wrong sometimes. Keep your algorithms humble and your datasets clean. I'm your host, signing off before an AI takes my job and does it better with a more pleasant voice and perfect comedic timing. Until next time, stay artificially intelligent, but naturally skeptical.