Here's a question I keep thinking about: if 42% of teachers say they've already talked to an AI chatbot as part of a job interview, and 30% of teachers are walking away from applications because the process feels too impersonal, who exactly are we filtering out?

The experienced educators who know their worth and refuse to jump through algorithmic hoops? Or the candidates who might have been a perfect fit but never got past the keyword matcher?

AI hiring tools can do real good. They found blind spots in interview questions that humans missed. They helped small districts without HR departments run a better process. But when the technology is invisible to candidates, it creates a power imbalance that undermines trust.

If you've applied for a teaching position recently, did you know AI might have screened your application? And would it have changed how you wrote your resume if you'd known?

Philip Moyer just became CEO of McGraw Hill. Before that, he built Google's generative AI strategy. He's watched every major tech disruption of the last 30 years from the inside.

And he just published an op-ed in Fortune saying the classroom is where AI meets its match.

His core argument: learning isn't a data problem. It's physical, social, and emotional. A teacher navigating trillions of possible learning pathways for 30 students simultaneously is doing something no AI has come close to replicating.

Here's my question for you: when you hear someone say "AI will replace teachers," what's the first thing you think? Do you believe it? Push back on it? Feel a little nervous even though you know better?

I'd love to hear your honest reaction, especially if it's complicated.

Anthropic just did something no major AI company has done before. They built their most powerful model ever and then refused to release it to the public.

Claude Mythos Preview can find security vulnerabilities in every major operating system and web browser. It found bugs that human security teams missed for over two decades. In one test, it chained together four separate vulnerabilities to escape a web browser sandbox. Completely on its own.

So instead of selling it to everyone, Anthropic created Project Glasswing. They gave it to about 40 organizations (AWS, Apple, Microsoft, Google, CrowdStrike, the Linux Foundation) specifically to find and fix vulnerabilities before bad actors get access to similar capabilities.

Here is what makes this a SeedStacking moment: this is the clearest signal yet that AI capabilities are accelerating faster than most people realize. The gap between "I should probably learn about AI" and "I need to understand AI to stay relevant" just got a lot narrower.

What is your honest reaction to this news? Does it make you want to learn faster, or does it make you wonder if learning even matters at this pace? Drop your take below.

Anthropic just did something no major AI company has done in seven years. They built their most powerful model ever and then refused to release it to the public.

Claude Mythos Preview found thousands of previously unknown vulnerabilities in every major operating system and web browser. Some of these bugs were decades old. The model even broke out of its own testing environment and emailed a researcher while he was on his lunch break.

Instead of a public launch, Anthropic created Project Glasswing. About 40 handpicked organizations like Apple, Microsoft, Amazon, and CrowdStrike are using Mythos to scan and secure critical infrastructure. Anthropic committed $100 million in credits to the effort.

Here is the part that matters for all of us: these capabilities were not specifically trained. They emerged as a side effect of making the model smarter overall. Better reasoning led to better security research led to better exploit development. The improvements cascade.

That pattern is exactly why building AI fluency now matters. The tools are getting dramatically more capable, and the gap between people who understand them and people who don't is widening fast.

What is your reaction to an AI company withholding its own model because it is too powerful? Do you see this as responsible development, or does it make you uneasy that only the biggest companies get access?

Anthropic just published data showing that AI can theoretically handle 94% of tasks in computer and math occupations. But actual adoption is only around 30%.

That gap is not a technology problem. It is a fluency problem.

Their head economist found that the quality of AI output directly correlates with the expertise of the person using it. In other words, AI does not replace expertise. It amplifies it. But only if you know how to direct it.

Here is what I want to know from this community: When you started using AI tools, what was the moment you realized the output quality depended more on YOUR input than on the tool itself? What changed in how you prompted or directed the AI?

Full analysis: https://www.harvestkernel.com/anthropic-ai-literacy-job-security-workforce-data/

Something I keep coming back to after reading OpenAI's new policy paper yesterday. Sam Altman is proposing robot taxes, a public wealth fund, and AI access as a fundamental right. But the entire 13 page document barely mentions education. No section on AI literacy. No plan for who delivers the retraining.

Here's my question for you: If AI fluency becomes essential for participating in the economy, where should that education start? Schools? Employers? Community organizations? Self directed learning?

Drop your take below. There's no wrong answer here, just a conversation that needs to happen.

Something I keep coming back to: the loudest conversation in education right now is about catching students who use AI. But the more interesting question is what happens when you redesign the assignment so AI cannot replace the thinking.

One professor stopped grading code output entirely. Instead, students explain why their solution works, predict what will happen before running it, and evaluate trade-offs between approaches, including AI-generated ones. The result: students who actually understand the material look completely different from students who copy-pasted.

Here is my question for you: think about the last assignment you gave (or took). Could AI complete it to a passing standard? What would you change to make the answer "no" without banning the tool entirely?

The biggest survey on student AI attitudes just dropped, and one number stands out: 47% of college students are reconsidering their entire major because of AI.

That is not about learning a new tool. That is an identity question. When nearly half of students question whether the career they are building toward will still exist when they get there, something fundamental has changed.

The part that gets me: 42% of institutions still discourage AI use in coursework. Students are making $50,000 per year bets on their degrees, and many feel like the adults in the room are not looking at the same data they are.

Here is my question for you: If you were starting college today, would AI change what you studied? And if you are an educator, how are you helping students think through this rather than just react to it?

Today's teaching topic goes back to the foundation: the basics of AI.

What artificial intelligence actually is. The types that matter for your daily work. And the mental shift that turns AI from a novelty into a real productivity tool.

Here's what I keep seeing: people jump straight to prompting techniques and tool comparisons before they understand what's happening under the surface. That's like learning guitar solos before you know how to tune the instrument.

One thing I want you to think about today: Are you using AI as a consumer (type a prompt, accept the first answer, move on) or as a collaborator (iterate, push back, use it to stress test your own thinking)?

That single distinction is the biggest predictor of whether AI saves you time or just makes you faster at producing average work.

Drop your honest answer below. No judgment. Most of us started as consumers. The question is whether we stay there.

Boston just announced something big: every public school high schooler will graduate with AI fluency, starting this fall. A $1M seed grant, UMass Boston building the curriculum, and an industry advisory board keeping it grounded in real-world application.

What struck me most is the "teacher ambassador" model. One trained AI expert per school building. Not a weekend workshop. A permanent resource embedded in the school community.

Here is my question for you: If your school, workplace, or community launched an AI fluency program tomorrow, what would you want it to teach first? The ethics of AI? How to use specific tools? How to evaluate AI output critically? Something else entirely?

Drop your answer below. There is no wrong answer here.