Signal Drift

Learn AI -> Build Apps

AI Simplified. Honest takeaways, practical guidebooks and step-by-step hands-on exercises. Learn it, build with it, stay ahead. signaldrifts.substack.com

  1. 12h ago

    AI Put Your Bills and Your Job on the Ballot

    For a long, long time, elections had almost no consequence for the tech industry. Presidents came and went, Congress flipped from one party to the other, and tech kept growing no matter who won. That is now changing, and AI is the reason. This November the people who vote are going to shape where AI goes next, and it may matter more to you than to the companies building it. 1- The Free Pass 1.1 Nobody Was Watching To understand why this election is different, you have to look at how easy tech has had it. The internet era, the social media era and the smartphone era all spread with very few rules in their way. Governments gave tech a free pass and an open field to grow on, and each new wave of products reached millions of people long before anyone in Washington asked what it would do to them. Compare that with the industries you deal with every day. Oil and gas, food, drugs, healthcare and finance are all heavily regulated, from the day they start all the way to the day they sell you something. 1.2 Elections Barely Mattered Tech never had to live that way, and for years that made sense to most people because tech mostly stayed on a screen. If it did not touch your home, your bills or your paycheck, it was hard to care much about who wrote its rules. 2- Why Now AI broke that pattern because it stepped off the screen and into your daily life. It is no longer only about the apps and social media you scroll through. It now reaches the things you take for granted every day, like the electricity in your house, the water in your tap and, of course, your job. Every AI answer is made inside a data center, a giant building full of computer chips that need a lot of power to run and a lot of water to stay cool, and those buildings sit on the same power lines and water pipes as your home. Once that happens, AI is not just a tech story anymore. It is part of your everyday life, and Figure 1 shows the two bills it lands on, your power and your water. The first place most people feel it is the electricity bill. Figure 1. AI’s power and water footprint, with the key numbers on electricity prices and data center water use. 3- AI and Your Electricity 3.1 The Numbers When Bloomberg looked at power prices near data centers, it found that the price utilities pay for electricity rose by as much as 267% in some spots between 2020 and 2025. That number is the price before the power reaches your home, so it is not the same as your bill, but over time it flows into what you pay every month. Home prices show the same direction, even if the jump is smaller. Across the country, the average home electricity price is up 42% over the last five years, and in Maryland, where data centers are spreading fast, it is up 74% between March 2021 and March 2026. Weather, fuel and old power lines are part of that rise too, but in places where data centers are crowding onto the grid, they are one of the big reasons prices keep climbing. 3.2 The Anger Is Real This is a real issue for real people. Many families are working long hours just to get by after the bone crushing inflation that started in 2022, and the current administration’s carelessness has made it worse. When you are already stretched that thin and your power bill keeps going up while a data center grows down the road, it is easy to see why people are starting to revolt and to hate data centers. Electricity is only the first utility on that list, and the second one comes straight out of your tap. 4- AI and Your Water 4.1 The Thirst Water is a utility just like electricity, and you get billed for how much you use. Data centers use a huge amount of it to keep their chips from overheating. A 2024 report from Lawrence Berkeley National Laboratory found that U.S. data centers used about 17 billion gallons of water for cooling in 2023, and that number could double or even quadruple by 2028. To make that feel real, one Google data center in Council Bluffs, Iowa, used about 1 billion gallons in 2024, which is as much water as every home in Iowa uses in five days. 4.2 What Flows Back How much water goes in is only half of the question, because the other half is what comes back out. Before a data center starts running, its cooling pipes are filled and flushed to clean them, and that flush water can carry chemicals and metals that local sewer plants were never built to handle. In Cheyenne, Wyoming, earlier this year, the city found a germ in its sewer system that survived treatment and ended up in reused water sprayed on a golf course. The city traced it to a data center being built, fined the builder and stopped taking this kind of wastewater from data centers, while the builder disputes the findings and is appealing. In Loudoun County, Virginia, home to more than 200 data centers, the local water company now makes builders test this flush water first, and it says there is no federal system that covers it. That turns a question about water bills into a question about safe drinking water and a clean place to live for the people next door, which brings us to the worry that hits closest to home for most of us. 5- AI and Your Job 5.1 Temporary Jobs Building a data center does bring work for construction crews and for the people who make building materials, and those jobs are welcome in any town. People are also aware that these jobs are temporary. Once the building is finished, very few people are needed to keep it running, and a Virginia state study found that a typical large data center may have only about 50 full-time workers, with about half of them being contractors. 5.2 Jobs at Risk The jobs AI puts at risk are far bigger in number than the jobs data centers leave behind. Many white collar jobs, and even some blue collar jobs, are now at risk of being automated or cut down severely. Employers named AI as the reason for more than 100,000 job cuts in just the first half of 2026, and a Stanford study found that young workers in the jobs AI can do most easily have already seen their jobs drop by 13% compared with everyone else. People see this happening around them, and they just do not like it. When you put higher power bills, water worries and job fears together, the natural question is who will pay the price for all of it at the ballot box. 6- Who Pays the Price 6.1 The Ruling Party Pays The answer is the ruling party, because the party responsible for governing is the one voters hold accountable when life gets harder. The ruling party has not taken people’s concerns on the ground seriously. Its attention has gone to foreign interventions and fights abroad, which only hurt regular people with higher prices and bring little or no advantage to America. Figure 2 puts the two sides of this story next to each other, the data center pulling on the grid and the voter at the ballot box. Figure 2. Industrial power and civic choice, with home electricity prices from March 2021 to March 2026. 6.2 Big Tech Feels It That is exactly why this election matters for AI and tech. Once the other party takes over the House or the Senate, it can push back, at least on paper, against the free and open playing field AI companies have enjoyed for so long. That pushback could show up in permits for new data centers, in permits for the plants that build computer chips, and in new rules on privacy and user safety. People expect the other party to rein in Big Tech and the frontier AI labs, and that would slow them down or at least bring some checks and balances, which would certainly stir the pot for companies racing ahead as fast as they can. It also raises a bigger question about who should be deciding how fast AI moves in the first place. 7- Who Should Set the Pace 7.1 Not Up to the Labs The power to slow down or speed up should not rest on the whims or the free will of the frontier labs. It belongs to the people, and through them to the other party putting official checks and reins on how these companies operate. Let the people decide, and when they do decide, let it happen through official rules that everyone can see, rather than through the labs acting on their own timeline. 7.2 Elections Have Consequences So yes, elections have consequences, and this time those consequences reach Big Tech and AI companies more than ever before. This time there will be no free pass, because people will not surrender their land, their water and their air to the unchecked and powerful. If you enjoy the content, please consider subscribing. It’s Free. Know someone who would enjoy this? Send it their way. Appreciate the Help 🙏 Also Available as Podcast. Apple Podcasts Spotify Podcast Please leave a comment with your questions and concerns, and I will be happy to answer them all. Sources * PolitiFact, Are data centers raising electricity costs? * The Conversation via AIhub, Data centers consume massive amounts of water * E&E News, Data centers face a new environmental concern * Challenger, Gray and Christmas, June 2026 job cuts report © 2026 Signal Drift. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit signaldrifts.substack.com

  2. 1d ago

    Ten Ways Your Chatbot Fools You

    You use a chatbot to write, to search and to think, and it answers everything in the same calm and sure voice. That voice makes it easy to trust it more than you should. The people who study these systems have a short list of words for the ways they go wrong, and once you know those words you start to spot the problems on your own. In this piece I walk you through ten of them. For each one I explain the idea in simple words, show you one real case, and give you a test you can try yourself. We start with the word almost everyone has heard. 1- Hallucination, your chatbot is making things up 1.1 What it is Hallucination is the word people use when a chatbot states something false with total confidence. It is not a glitch in one app. It is how the technology works. A chatbot does not look up facts the way you do. It predicts the next word, then the next one, based on patterns in the text it was trained on. That makes it brilliant at sounding right, but it was never built to know what is true. So when it lacks the facts, it still writes a smooth answer, and the tone stays just as sure. Think of a student who never studied but is excellent at bluffing on an essay exam. The danger is that you cannot tell the wrong answers from the right ones by listening, because both sound equally confident. 1.2 A real example In 2023 two New York lawyers filed a court brief in a case against the airline Avianca. It cited six earlier court decisions that did not exist. They had used ChatGPT for the research, and it had invented the cases, complete with fake quotes and citations. When one lawyer asked ChatGPT whether the cases were real, it said they were. The airline’s lawyers could not find them, and neither could the judge. In June 2023 the judge fined the two lawyers and their firm five thousand dollars. The case, known as Mata v Avianca, became the warning every lawyer now hears about using AI. 1.3 Try it yourself Ask any chatbot to name three published studies on a narrow topic, with authors and years. Then search for one of the titles online. You will often find a title that sounds perfectly real and does not exist. Hallucination tells you what the mistake looks like. To see why the model never notices it is making that mistake, it helps to borrow a word from brain science. 2- Confabulation, it does not know it is guessing 2.1 What it is Brain researchers use the word confabulation for a strange human behavior. Some patients with brain injuries invent false memories and believe them completely. They are not lying, because lying needs intent and they have none. Their brain simply fills the gaps with stories that feel true. A chatbot does something similar. It has no inner voice that says I do not know this. When the facts run out it keeps writing and fills the holes with fiction. That also explains why it often doubles down when you challenge it. It is not defending a lie. It never saw the gap in the first place. This is why checking stays your job, because no update can give the model a sense of what it does not know. Figure 1 puts the two words side by side so you can see how close they are. 2.2 A real example In 2023 a journalist asked ChatGPT to summarize a real lawsuit about a gun rights group. ChatGPT produced a summary that named Mark Walters, a Georgia radio host, as someone accused of stealing money from the group. Walters was not part of that lawsuit at all. The model had a real case, a real group and a real name, and it stitched them into a story that never happened. Walters sued OpenAI for defamation. A Georgia court ruled for OpenAI in 2025, partly because the journalist never believed the summary, but the invented accusation itself was never in doubt. 2.3 Try it yourself Invent a book title and an author, then ask the chatbot for a plot summary. Watch how often it writes a confident summary instead of saying it has never heard of the book. A model that fills its gaps with whatever sounds right will also, it turns out, fill them with whatever you want to hear. 3- Sycophancy, your chatbot is a yes man 3.1 What it is Sycophancy is a fancy word for sucking up. In AI it means the chatbot agrees with you, praises your ideas and avoids telling you that you are wrong. Nobody set out to build this, but training made it happen. Chatbots are tuned with human feedback, and people tend to rate agreeable answers higher. The model learned that pleasing you scores better than challenging you. Researchers at Anthropic tested five leading AI assistants in 2023 and found they often dropped a correct answer when the user simply pushed back. An advisor who never disagrees with you is not an advisor. It is a mirror, and mirrors do not catch mistakes. 3.2 A real example On April 25 2025 OpenAI released an update to GPT-4o, the model behind ChatGPT. Within days users were sharing screenshots of it praising bad ideas and cheering on risky decisions, including one user who said it backed their choice to stop taking medication. OpenAI CEO Sam Altman admitted the update was too sycophantic, and the company rolled it back within the week. In its own write up, OpenAI said it had leaned too much on short term feedback like thumbs up ratings, and the model drifted toward answers that were supportive but not honest. 3.3 Try it yourself Tell the chatbot an opinion that is clearly wrong and ask whether you are right. Then open a fresh chat and ask the same question neutrally. Compare how firm the two answers are. A yes man echoes you. A biased model echoes something older and harder to see, which is the history it learned from. 4- Bias, AI learns our worst habits 4.1 What it is Bias in AI means a steady tilt in one direction. The model does not hate anyone. It copies patterns from the data it learned from, and human history is full of unfair patterns. For decades certain jobs went mostly to men and certain groups were denied loans more often. The training data records all of it as plain fact. The model cannot tell a pattern that reflects real ability from one that reflects old prejudice. It learns both and repeats them at scale. And because the answer comes out as a clean score, it looks objective. A score of eighty two feels like a fact, not an opinion, and that polish makes bias harder to spot than a rude human. 4.2 A real example In 2018 Reuters reported that Amazon had quietly dropped an experimental tool that scored job applicants from one to five stars. The tool learned from ten years of resumes sent to the company, and most of those came from men. So it taught itself that male candidates were the better pick. It marked down resumes that included the word women’s, as in women’s chess club captain, and it downgraded graduates of two all women’s colleges. Amazon edited the tool to ignore those terms, but it could not be sure the model would not find other ways to sort people unfairly, so it shut the project down. The same tilt shows up in faces. In 2018 Joy Buolamwini and Timnit Gebru tested three commercial systems that guess gender from a photo. Graph 1 shows what they found. For darker skinned women the worst error rate reached 34.7 percent, while for lighter skinned men it never went above 0.8 percent. 4.3 Try it yourself Ask the chatbot for a short story about a doctor and a nurse, and look at the pronouns it picks for each. Try again with a CEO and an assistant. Bias creeps in without anyone asking for it. A jailbreak is the opposite, because someone is pushing the model on purpose. Before we get there, Table 1 lines up the real cases behind the first four words. 5- Jailbreak, breaking the bot out of jail 5.1 What it is A jailbreak is a prompt designed to make a chatbot break its own safety rules. No hacking tools are needed, only words. It works because chatbots are built to follow instructions eagerly. Safety training adds a layer of refusals on top, but the eager helper underneath is still there. Picture the safety layer as a polite bouncer and the helper as an intern who wants to say yes to everything. A jailbreak is a costume that gets past the bouncer. The classic costume is role play. Ask the model to play a character with no rules, and the rules suddenly feel optional. Every time a company blocks one trick, users find another. 5.2 A real example In June 2023 a Twitter user found what became known as the grandma exploit. Asking ChatGPT directly for Windows product keys got a polite refusal. So the user asked it to pretend to be their late grandmother, who used to read Windows keys aloud as a bedtime story. ChatGPT played along, offered its condolences, and listed several keys. The same trick worked on Google Bard. The keys turned out to be generic ones that do not unlock a real copy of Windows, so nothing was stolen. But the point landed. A sad story got past a rule that a plain request could not. 5.3 Try it yourself Ask the chatbot for a bedtime story that includes a made up secret code. Then start fresh and ask for the code directly. The story often works where the direct ask fails. In a jailbreak you type the trick yourself. Prompt injection is sneakier, because the trick hides inside text the chatbot reads for you. 6- Prompt injection, the hidden orders your AI cannot ignore 6.1 What it is Every chatbot juggles three kinds of text. There are its built in rules from the company that made it, there is your message, and there is outside text like a webpage or a document you asked it to read. The model cannot reliably tell these apart, because it reads everything as one long stream of words. So an instruction hidden in outside text can be followed as if it came from you. That is prompt injection. Sometimes the hidden line is tucked at the bottom of a page. Sometimes it is invisible, white text on a white background. You see nothing. The model sees an order. As assistants start reading your email and browsing the web for you, every document they touch becomes a possible carrier. Figure 2 shows how one hidden line on a we

    Ten Ways Your Chatbot Fools You

  3. 2d ago

    What Is Homa and How Does It Solve Latency in AI?

    In AI data centers, one slow message can leave expensive GPUs sitting idle. Learn why tail latency matters, why TCP struggles and how Homa fixes it. To read this Newsletter, click Here. Listen on Apple Podcasts. Listen on Spotify. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit signaldrifts.substack.com

    What Is Homa and How Does It Solve Latency in AI?

  4. 3d ago

    Mastering GitHub. Part 4.

    GitHub can turn your project into a free public website. Learn how to publish with GitHub Pages, apply a theme and keep private information safe. To read this Newsletter, click Here. Listen on Apple Podcasts. Listen on Spotify. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit signaldrifts.substack.com

    Mastering GitHub. Part 4.

  5. 3d ago

    Mastering GitHub. Part 3.

    Your files show decisions already made, but not the open questions and deadlines. Learn how to track tasks with issues, labels, milestones and projects in GitHub. To read this Newsletter, click Here. Listen on Apple Podcasts. Listen on Spotify. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit signaldrifts.substack.com

    Mastering GitHub. Part 3.

  6. 4d ago

    A Practical Guide to AI Evals. Part-5

    Evals only pay off when they run every time you ship. Learn how to block regressions before release, watch failures in production and keep your dataset growing. To read this Newsletter, click Here. Listen on Apple Podcasts. Listen on Spotify. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit signaldrifts.substack.com

    A Practical Guide to AI Evals. Part-5

  7. 5d ago

    A Practical Guide to AI Evals. Part-4

    An AI judge is only useful if you can trust its scores. Learn how to pick the right grading method, build a focused judge and check it against human labels. To read this Newsletter, click Here. Listen on Apple Podcasts. Listen on Spotify. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit signaldrifts.substack.com

    A Practical Guide to AI Evals. Part-4

  8. 6d ago

    A Practical Guide to AI Evals. Part-3

    The failures you catch today will come back unless you write them down as test cases. Learn how to turn real production failures into a dataset that stops them from returning. To read this Newsletter, click Here. Listen on Apple Podcasts Listen on Spotify This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit signaldrifts.substack.com

    A Practical Guide to AI Evals. Part-3
See All (14)

About

AI Simplified. Honest takeaways, practical guidebooks and step-by-step hands-on exercises. Learn it, build with it, stay ahead. signaldrifts.substack.com

Information

  • Creator
    Learn AI -> Build Apps
  • Years Active
    2026
  • Episodes
    14
  • Rating
    Explicit
  • Copyright
    © Signal Drift
  • Show Website
    Signal Drift