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*TRUMP: MET OWN GOALS ON IRAN CONFLICT, WEEKS AHEAD OF SCHEDULE

Sam Altman said people saying “please” and “thank you” to ChatGPT costs OpenAI tens of millions of dollars a year in compute. 67% of Americans do it anyway. Run the math on why. A 2024 Waseda University study tested LLM responses across politeness levels in English, Chinese, and Japanese. Impolite prompts produced measurably worse outputs: more bias, more errors, more refusals. Moderate politeness consistently beat both extremes. The mechanism makes sense once you see it. Polite prompts pattern-match to higher-quality training data. When you write “Could you help me structure this analysis?”, the model pulls from professional, well-reasoned text. When you write “give me the answer,” it pulls from Reddit. Google DeepMind’s Murray Shanahan explained it simply: the model is role-playing a smart intern. Treat the intern like a colleague, you get colleague-quality work. Bark orders, you get minimum-viable compliance. Now look at the cost side. OpenAI handles over a billion queries daily. Each GPT-4 query uses roughly 2.9 watt-hours, ten times a Google search. But OpenAI just raised $40 billion at a $300 billion valuation. Tens of millions in politeness tokens is a rounding error on a rounding error. 67% of users do it anyway, and 55% of them say it’s because it’s “the right thing to do.” They’re maintaining a behavioral habit that governs every other interaction in their life. The parent who teaches their kid to say please to Alexa isn’t doing it for Alexa. They’re doing it because the alternative is raising someone who learns that being rude gets faster results. Telling 900 million people to stop saying thank you so OpenAI can save 0.01% of operating costs is the most engineer-brained optimization take on the internet. You’re training yourself to treat every interaction as a transaction. And that habit doesn’t stay in the chat window.

🚨 THE FIRST “LIVING COMPUTER” HAS ARRIVED Scientists are now running computers on lab-grown human brain cells. Real neurons. Growing on silicon chips. Companies like Cortical Labs are building biocomputers using tiny brain organoids derived from stem cells. These clusters contain around 200,000 to 800,000 neurons and connect to electrodes that allow them to receive inputs and send signals back. And they can already learn. Researchers have trained them to play simple games like Pong by responding to electrical feedback. The wild part is the efficiency. A human brain runs on about 20 watts. Modern AI data centers consume megawatts. That massive energy gap is pushing scientists to explore biological computing. Cortical Labs has already launched a commercial system called CL1. It sells for around $35,000 and lets developers run code directly on living neuron clusters through the cloud. They call it “Wetware as a Service.” The vision is hybrid computing. Silicon handles raw speed. Biology handles adaptive learning. Your brain can learn from small amounts of data, adapt instantly, and even repair itself. Traditional AI needs enormous datasets and constant retraining. If this technology scales, future data centers could be filled with “living servers.” Networks of biological processors working alongside traditional chips. Some researchers believe systems like this could be hundreds of millions of times more energy efficient for certain tasks. But scaling it will be difficult. The human brain has roughly 86 billion neurons. Today’s biocomputers have less than a million. Connecting billions of neurons across data centers would require entirely new hardware. And there are big ethical questions. How do you maintain living neurons in server racks? Could systems like this ever become conscious? And are we comfortable using biological intelligence as computing infrastructure? One thing is clear. AI’s energy demand is exploding. Data centers could consume up to 8% of global electricity by 2030. If biological computing works, the future of AI might not run purely on silicon. It could run on living neural networks.