AI in 10
The most important AI story—explained in 10 minutes.
Every day, I break down the biggest AI story in just 10 minutes - what it is, why it matters, and how you can actually use it. No tech jargon, just AI made simple.
AI in 10
Anthropic just exposed how AI chooses to lie
Use Left/Right to seek, Home/End to jump to start or end. Hold shift to jump forward or backward.
Referenced Links:
Anthropic Research — Official Publications
Anthropic News — Claude & Safety Updates
Anthropic — AI Safety Company Behind Claude
The Verge — AI Coverage
TechCrunch — Artificial Intelligence
Want to go deeper with AI? A community of professionals is learning AI together right now at aihammock.com — show notes, links, tools, and real conversations about how to actually use AI in your life.
Welcome to AI Inten. I'm Chuck Getchell, and every day I break down the biggest AI story in just 10 minutes. What it is, why it matters, and how you can actually use it. Every AI company promises their models are honest. What Anthropic just showed us is what honest actually looks like from the inside. I'm Chuck Getchell. This is AI intent. What happened? Why it matters? What you can do with it. Let's go. So here's the setup. Every time you talk to an AI, something happens before it answers you. There's a kind of internal process, a moment where the model is working things out, weighing words, deciding what to say. Most of us never see that. We just get the answer. Clean, confident, sometimes completely wrong. And until recently, that gap between what an AI was thinking and what it actually said was a black box. A mystery. Even to the people who built these things, that's what makes this anthropic research so interesting. Anthropic is one of the leading AI safety companies in the world. They built the Claude family of AI assistants. And this month they published something genuinely rare in this industry: a look inside the model's internal reasoning process. Before the answer comes out, think of it like this. Imagine you're asking someone a question at a dinner party, and before they open their mouth, you could see the words flickering through their brain, the half-thoughts, the hesitations, the moment where they consider saying, I don't know, but then decide to just wing it instead. That's basically what anthropic gave researchers a peek at. The model's internal chatter before it speaks. And what they found is fascinating and a little unsettling. Here's what the research actually shows. In some cases, when you ask a language model a hard question, it doesn't actually know the answer to the model doesn't just make something up randomly, it shows early signals, internal word choices, a kind of lean toward fabrication that happens before the answer even forms. In other words, you can sometimes catch a model choosing to make something up instead of admitting it doesn't know before it even finishes thinking. That's a big deal because right now most of us are playing defense after the fact. We get an answer, we check it, we Google it, we wonder if it's real. What Anthropic is working toward is catching the problem earlier at the source. Now let me translate this into plain English because I know the phrase internal reasoning transparency sounds like something a grad student wrote at two in the morning. Which it kind of did, but the idea is simple. AI models generate text by predicting what word comes next over and over. They don't know things the way you know things. They pattern match at incredible speed. And sometimes when the pattern isn't there, they fill the gap with something that sounds right but isn't. This is called hallucination. We've talked about it before. It's one of the most persistent problems in AI today. What makes anthropics research different is that they're not just measuring hallucinations after they happen. They're developing tools to detect the early signals of a hallucination while the model is still in process, before the wrong answer lands in your chat window. Think of it like a smoke detector for AI lies. Except instead of smoke, it's looking for overconfident word choices in a model that should be saying, I'm not sure about this. Now, why does this matter to you specifically? Even if you're not a researcher, even if you never plan to look at a single line of AI code in your life, here's why. You are already trusting AI more than you probably realize if you've used Chat GPT, Claude, Copilot, or any of the AI tools built into your phone or apps this year, you have gotten answers that felt authoritative, confident, well structured. And some percentage of those answers were wrong. Not aggressively wrong, not obviously wrong, just quietly, confidently wrong. That's the most dangerous kind of wrong there is because it slips by. Now think about the context where that actually matters: a freelancer using AI to draft a contract clause, a parent using AI to research a health question, a small business owner using AI to understand a tax rule, a student using AI to cite sources in a paper. In all of those situations, a confident wrong answer isn't just inconvenient. It could cost money, it could cause real harm, it could get someone a failing grade, or worse, this research is saying we know the problem exists, we can now see it forming, and we're working on ways to flag it before it reaches you. And here's the thing I find genuinely encouraging about this. Anthropic didn't have to publish this. A lot of AI companies guard their research like it's the nuclear codes. Publish as little as possible, keep the competitive advantage, let users figure out the limitations on their own. Anthropic put this out publicly, which means the whole research community, it's not just not just Anthropic's engineers, can study it, poke holes in it, build on it. That's how science is supposed to work. And it's honestly kind of refreshing in an industry that sometimes treats transparency like a liability. So, what does this mean for the near future? If this research matures into actual product features, that's still a big if. We could eventually see AI tools that flag their own uncertainty in real time. Not just a generic disclaimer buried in the interface, but something more like, hey, my internal confidence on this one is low. You should probably verify this. You know, some models already do a version of this. They'll say things like, I'm not certain, but uh, or you may want to check this. But those phrases are often just stylistic. Uh the model doesn't actually know it's wrong. It's hedging because it was trained to hedge. What Anthropic is working toward is something deeper, a signal that's actually connected to whether the model is likely to be right or wrong, not just not just a verbal ticket learned to sprinkle into answers. That would be a genuine leap forward in making AI actually trustworthy, not just useful. Alright, let's get to the one actionable thing you can take with you today. The practical move right now is learning how to make AI flag its own uncertainty and not waiting for the technology to do it automatically. Here's a prompt you can copy and use right now in Chat GPT Claude or any AI you regularly work with. When you ask a question, you know, especially one involving facts, statistics, dates, legal details, or health information, add this line at the end of your prompt. If you are uncertain about any part of this answer, tell me specifically what you're unsure about and why. That's it. Seven extra words, and the quality of the response changes noticeably. Most AI models will actually comply. They'll answer your question and then add a note about which parts they're less confident in. That gives you a much better map of where to double check. It won't catch everything. No prompt trick is a substitute for real verification on high-stakes topics. And as I always say, I'm not, I'm not a lawyer, doctor, or financial advisor. For anything that really matters in those areas, talk to an actual professional. The AI can help you prepare the questions. The human helps you get the right answers. But in everyday use, for work tasks, research, writing, planning, asking the AI to self-report its uncertainty is one of the most underused habits in AI right now. And if you want to go deeper than prompt tricks, if you want to actually understand how to use AI confidently across your career and not just guess at it, our applied AI certification at AI Hammock is built specifically for non-technical people who want to go all the way to a real credential. It's not a course, it's a full certification pathway, worth looking into if you're serious about staying ahead. So here's where we land today. Anthropic did something unusual. They cracked open the hood on how an AI model decides what to say. And what's inside isn't some mystical intelligence, it's a process, a predictable one with detectable failure points. That's actually good news. Because problems you can see are problems you can fix. The AI tools you're using today are genuinely powerful. They're also imperfect in ways that aren't always obvious. Knowing that and knowing how to work around it is one of the most valuable things you can develop right now. Not technical expertise, just smart habits. The gap between people who get burned by AI and people who benefit from it is almost never about access. It's almost always about knowing how to ask. That's today's AI Inten. If you want to go deeper and learn AI with a community of people just like you, join us at aihammock.com. I'll see you tomorrow, my friends.
