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| Welcome Automaters, 👋 | So back in February, Sam Altman basically told the world that some companies blaming AI for their recent layoffs are... not exactly telling the whole truth. He calls it "AI washing." | Think of it like greenwashing, but instead of pretending to save the planet, companies are pretending AI "synergized" their workers out of a job. In reality? They just wanted to cut costs and needed a cool, futuristic scapegoat to soften the PR blow. | What Did Sam Actually Say? | "I don't know what the exact percentage is, but there's some AI washing where people are blaming AI for layoffs that they would otherwise do," Altman told CNBC-TV18. | The Translation: Some bosses fired people for garden-variety downsizing, then pointed at a chatbot and said, "It was him!" | Now, Altman isn't letting his own creation off the hook completely. He does expect the real impact of AI on jobs to become "palpable" within the next few years. He’s also playing the long game; predicting that while old roles vanish, brand-new types of careers will emerge, just like every other major tech shift in history. | And hey, if you think Sam is just being dramatic, the data actually has his back. A study from the National Bureau of Economic Research found that nearly 90% of surveyed executives across four countries including: U.S., the U.K., Germany, and Australia admitted that AI had zero impact on their hiring levels over the past three years. | Yet, curiously, every other layoff "Breaking News" headline claims the robots are to blame. Something isn't adding up, right? | The Yale Budget Lab also took a look at the numbers through November 2025 and found exactly... nothing. One economist put it bluntly: "No matter which way you look at the data, at this exact moment, it just doesn't seem like there's major macroeconomic effects here." | The Bottom Line | Some companies are using AI as a very convenient, high-tech cushion to hide old-fashioned, "we-spent-too-much-money" downsizing. It’s a lot easier to blame a sophisticated algorithm than it is to admit to poor management. | Here's what we have for you today | | | | | | | 🤕 AI Out-Diagnosed Two Real Doctors in the ER | | The ER got a very unexpected new colleague folks | Researchers at Harvard Medical School and Beth Israel Deaconess Medical Center just published a study in Science (yes, the big-deal peer-reviewed journal), and the results are honestly a little jaw-dropping. They put OpenAI's o1 model head-to-head against two attending physicians in a real-world emergency room setting. | The goal? See who could actually figure out what was wrong with the patients first. | Here’s how the "Medical Smackdown" went down | The setup was simple but brutal: 76 patients arrived at the Beth Israel ER. Two internal medicine doctors, OpenAI’s o1, and the older 4o model were all handed the exact same electronic health records. We’re talking raw vitals, demographic info, and those brief, messy nurse's notes. | To keep things fair, two other doctors graded the diagnoses without knowing who, or what, wrote them. | The results? Honestly, it wasn't even a fair fight: | | That gap is widest right at triage; which, as we all know, is the absolute highest-stakes moment in emergency care. | Here’s the cherry on top: the researchers confirmed they did not "clean up" or pre-process the data for the AI. The model had to dig through the same messy, real-world records the human doctors had. So yeah, it wasn't fed a perfect script; it had to deal with the chaos of a real hospital. | So, are doctors out of a job? | Not exactly. While experts are calling this progress "real," the path to actually using this in a hospital is still a giant question mark. As one expert from Mount Sinai put it: "The open question is how the heck do you introduce it into clinical workflows in ways that actually improve care?" | It’s a fair point. Even the Harvard researchers are being very careful to say there is zero evidence that AI should replace doctors. This is about AI being the ultimate "super-assistant"—the kind of partner that catches what a tired, overworked human might miss at 3:00 AM. | But here’s another question: Would you trust a chatbot to triage you in the ER, or do you still need to see a human in a white coat to feel safe? | Are we ready for "Doctor GPT," or is this getting a little too close for comfort? | Learn more here. |
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| | | | | | | | | | 🤖 AI Workout Of The Day: How To Turn FAQs into Persuasive Sales Copy | | Your FAQs aren’t just a list of answered questions, they’re a goldmine for sales. | They reveal real objections or hesitations, so by answering them clearly and confidently, you can address concerns before they arise, build trust, and guide potential customers toward a purchase. | This approach saves you time and strengthens your messaging by turning genuine concerns into powerful sales points. | Here’s how to use this strategy effectively: | Choose Real, Frequent Questions: Pick questions you actually receive often to make the copy relevant and authentic. Focus on Objections: Identify concerns or doubts behind the questions and address them head-on in your sales copy. Use Clear, Reassuring Language: Your tone should ease worries and build confidence in your product or service. Include Calls to Action: After answering, encourage readers to take the next step—which could be to buy, contact, or learn more. Keep It Conversational: Write like you’re talking to a friend to make the copy engaging and relatable.
| 💡 Prompts to try: | Turn these 3 common questions I get into persuasive sales copy that removes objections and builds trust. For each question:
1. Identify the underlying concern or objection behind the question.
2. Address that objection clearly and confidently using reassuring, easy-to-understand language.
3. Use a conversational tone that sounds friendly and relatable, as if speaking directly to a potential customer.
5. Include a strong call to action at the end of each response that encourages the reader to take the next step (such as buying the product, contacting us, or learning more).
5. Make sure the sales copy feels authentic by basing it on real, frequently asked questions to ensure relevance and trustworthiness.
Here are the questions: [insert questions here]
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