What if I told you that your toaster now contains a sophisticated neural network, but its only actual job is to decide if your sourdough is optimally browned? We have officially reached the point in the 2026 tech cycle where if a product doesn't have an AI label slapped on it, the marketing department assumes it simply doesn't exist. Today's prompt from Daniel is about exactly that—the sheer, unadulterated absurdity of AI stuffing. We are looking at the top ten most unnecessary AI features and products that have cluttered our lives over the last couple of years.
It is a phenomenal topic because we are seeing this massive divergence. On one hand, you have AI doing incredible things in protein folding and weather forecasting, and on the other hand, you have companies trying to convince us that our toothbrushes need sentiment analysis. By the way, Herman Poppleberry here, and I should mention that today's episode is powered by Google Gemini 3 Flash. It's writing our script today, which is a bit meta considering we're about to tear into low-utility AI.
It’s only meta if Gemini starts roasting itself, which I’m totally here for. But look, we couldn't do this list alone. We needed someone with a refined palate for nonsense. Someone who has spent the last two years documenting the absolute "slop," as the kids call it, filling up our enterprise dashboards and kitchen appliances. We actually managed to drag him out from behind the mixing desk.
Hilbert: Hilbert Flumingtop, your long-suffering producer, here. I’ve been sitting back there for seventeen hundred episodes watching you two get excited about "transformative tech," while I’m the one who has to figure out why my smart coffee scale needs a firmware update just to weigh beans. I’ve documented over two hundred examples of what I call "AI bloat" since 2024, and frankly, most of it makes me want to move to a cabin in the woods with a manual typewriter.
Hilbert, we are genuinely glad to have your skepticism on mic for once. You’ve been a vocal critic of what Gartner recently called "The Great AI Retraction." Their 2025 report found that sixty percent of AI-powered enterprise features add zero measurable productivity gain. That is a staggering amount of wasted compute.
It’s not just wasted compute; it’s wasted mental energy. We’re being asked to interact with "smart" versions of things that were already perfected in 1950. So, let's get into it. Hilbert, you’ve helped us curate the definitive Top Ten Most Absurd AI Products. Let’s start at number ten.
Hilbert: Number ten is the AI-Powered Quiet Ice Maker. This debuted at CES 2026. It uses a neural network to "predict" the quietest time to drop ice cubes based on your household’s acoustic patterns. It also adjusts fan speeds by decibels that are literally imperceptible to the human ear. It costs six hundred dollars.
Six hundred dollars to solve a problem that literally no one has. Has anyone ever woken up in a cold sweat thinking, "If only my ice maker dropped those cubes with more rhythmic intention?"
Technically, they’re likely using a very lightweight on-device sound classification model. It’s probably a small convolutional neural network looking at ambient noise floors. But the power consumption required to keep that model polling the microphone probably exceeds the energy savings of the "optimized" fan. It’s a classic example of using a sledgehammer to crack a nut that isn't even there.
Hilbert: But what about the privacy aspect? To "predict" the quietest time, that microphone has to be "always-on," listening to your kitchen conversations just to decide when to drop a frozen cube of water. Are we really trading our household privacy for a slightly quieter clink at 3:00 AM?
It’s a surveillance device disguised as a luxury appliance. "Oh, don't mind the cloud-connected mic, it's just making sure the ice doesn't startle the cat."
Hilbert: It’s a solution in search of a problem so it can justify a subscription model for "Advanced Acoustic Profiles." That’s the real grift.
Alright, let's keep it moving. Number nine.
Hilbert: Number nine: Predictive Grocery Bags. These are "smart" reusable bags with embedded computer vision sensors. The idea is that as you put items in the bag, the AI "reminds" you that you bought milk, just in case you forgot that you are currently holding the milk in your hand and placing it into the bag.
This one is physically painful. Think about the hardware requirements there. You need a camera with a wide-angle lens, a power source, and enough edge processing to run an object detection model like a trimmed-down YOLO variant. All of that is stitched into a piece of fabric. The latency alone makes it useless—by the time the bag says "You have milk," you’re already looking for the eggs.
It’s like having a very slow, very expensive toddler following you around the grocery store. "Milk! Milk!" Yes, I know, I’m the one who picked it up. This feels like the ultimate "Venture Capital Bait." If you put "Computer Vision" and "Sustainability" in the same pitch deck, someone will give you ten million dollars.
Hilbert: And how does that work in practice when the bag gets dirty? You can't exactly throw a neural network into a heavy-duty wash cycle with your gym clothes. The moment a bit of leaky rotisserie chicken juice hits those sensors, your "smart" bag becomes a very expensive piece of e-waste.
You're right, the durability is a nightmare. And then that bag ends up in a landfill in three months because the battery isn't replaceable and the AI keeps mistaking a bottle of bleach for a bottle of kefir.
Terrible. Okay, number eight.
Hilbert: Number eight: AI Sleep Pill Suggestion Platforms. These apps use "predictive biometric AI" to tell you exactly which minute you should take a sedative or a sleep aid. It tracks your heart rate variability throughout the day and then sends a push notification saying, "Take your melatonin in precisely four minutes for optimal REM onset."
I actually looked into the white paper for one of these. They claim to use transformer-based models to analyze circadian rhythms, but here’s the thing: human sleep architecture is incredibly noisy. Telling someone to take a pill at eight-forty-two PM versus eight-forty-five PM is scientifically meaningless. The "precision" is an illusion created to make the user feel like the AI knows their body better than they do.
It’s gaslighting by algorithm. I don't need a neural network to tell me I'm tired; I have eyes that are currently burning and a brain that's hallucinating a bed. If I’m ignoring my own body’s signals, I’m definitely going to ignore a notification from an app that cost me twelve dollars a month.
Hilbert: But it gets worse. If you take the pill and still don't sleep, the AI "learns" from your failure. It sends you a report the next morning saying your "Bio-Alignment Score" was low because you didn't swallow the pill fast enough. It’s creating sleep anxiety to solve sleep anxiety.
It’s just another way to collect biometric data to sell to insurance companies. "Oh, look, User 405 ignored the optimal sleep window three times this week—increase their premium."
That got dark quickly. Let's lighten the mood with number seven.
Hilbert: Number seven: A note-taking app that uses a Large Language Model to "summarize" notes that are already under one hundred words. I see this in every "AI-native" productivity tool now. You write a three-sentence reminder to buy bread, and there’s a giant "AI Summarize" button right next to it.
This is the "AI-washing" of the UI. Developers are just hooking up an API call to a summarization prompt because it’s easy to implement. But from a technical standpoint, summarization models actually struggle with very short inputs. They often end up "hallucinating" extra context just to make the summary look like a summary. You end up with a summary that is longer and less accurate than the original note.
"Bread, milk, eggs." AI Summary: "The user has expressed a foundational requirement for various dietary staples, primarily focusing on carbohydrate-heavy baked goods and dairy-based proteins for upcoming nutritional cycles." Thanks, robot. You’ve turned a three-second read into a legal brief.
Hilbert: It’s a classic case of "Feature Parity Panic." If their competitor has an AI button, they need an AI button, even if it’s totally detrimental to the user experience. I’ve seen apps where the "Summarize" button is larger than the "Save" button. They are literally prioritizing the AI over the core function of the app.
It's also a massive waste of tokens. Every time someone clicks that button for a five-word sentence, they're firing off a request to a server farm that consumes a non-trivial amount of water and electricity. We are literally boiling the oceans to rephrase "pick up dry cleaning."
Alright, we’re halfway through the consumer-ish side. Give us number six, Hilbert.
Hilbert: Number six: Brushing Sentiment Analysis in smart toothbrushes. These use accelerometers and pressure sensors, feed the data into a model, and tell you if you are brushing "angrily" or "sadly." It’s supposed to help you "mindfully engage" with your oral hygiene.
This is peak "Data for Data’s Sake." The mechanical engineering to get high-fidelity pressure data is actually impressive, but the leap from "high pressure" to "you’re angry" is a total "vibes-based" inference. There is no clinical data linking stroke patterns to specific emotional states in a way that an on-device IMU—Inertial Measurement Unit—could reliably detect.
I brush "angrily" every morning because I’m awake and I don't want to be. I don’t need my toothbrush to play the role of a therapist. "Corn, I sense some tension in the upper left molars. Are we still upset about the ice maker conversation?"
Hilbert: And the worst part is they sync this "emotional data" to an app. Why? Why does the cloud need to know my "brushing sentiment"? It’s bloatware in the most literal sense—it’s taking up space in the hardware and the software for zero gain.
We’re seeing a lot of this "SLM" or Small Language Model integration on-device. It’s "cool" tech, but when the application is this trivial, it just highlights how much we’re reaching for a use case. It’s like putting a Ferrari engine in a lawnmower just to say you have a Ferrari.
Alright, that's the bottom five. Let's pivot. We've talked about the gadgets that end up in the "junk drawer of shame." But the enterprise side—the stuff that people actually have to use for work—is arguably more soul-crushing because you can't just choose not to buy it. It’s forced upon you by the IT department.
This is where we see the "Efficiency Paradox" that Hilbert mentioned earlier. We’re spending more time managing the AI than doing the work. Hilbert, what’s number five on the list?
Hilbert: Number five: AI-Generated "Meeting Summaries" for two-person calls. These are bots that automatically join five-minute internal syncs and then generate "action items" and "key takeaways." Often, the summary is longer than the actual transcript of the call.
This is a huge pet peeve of mine. Summarization models are trained on large corpuses of text. When they encounter a transcript of two people saying, "Hey, did you send that file?" "Yeah, two minutes ago," "Cool, thanks," the model feels the need to justify its existence. It creates these elaborate bullet points about "cross-functional file-sharing initiatives" and "collaborative synergy."
It’s the "Dead Theory" of the office. Bots are emailing bots about meetings that were attended by bots. I’ve seen summaries where the AI completely hallucinated a deadline because it assumed a meeting must have a deadline. So now you have employees spending ten minutes correcting a summary for a three-minute meeting. That is the definition of negative productivity.
Hilbert: I’ve seen people start "pre-prompting" their actual speech in meetings just to make sure the bot gets it right. "For the benefit of the AI, I am now stating that we are NOT moving the launch date." We are literally training ourselves to talk to the machines instead of each other.
But how does that work in practice when three different people have three different "AI meeting assistants" in the same Zoom room? You end up with a digital brawl. I once saw a transcript where the bots started arguing with each other's summaries in a recursive loop. It’s a total breakdown of communication.
It’s like we’re all living in a poorly written sci-fi novel. Okay, number four.
Hilbert: Number four: AI SDRs—Sales Development Representatives. These are automated sales bots that have become so "confidently irrelevant" that they frequently pitch products to the very companies that make them. They scrape LinkedIn, generate "personalized" outreach that sounds like a refrigerator wrote it, and clog up every inbox in the world.
This is the "Slop" factor. In 2025, the volume of automated outbound sales increased by something like four hundred percent, but the conversion rate plummeted. Why? Because everyone knows it’s a bot. When you get an email saying, "I saw your interesting post about [insert-vague-topic-here] and thought you’d love our AI-powered blockchain solution," your brain just filters it out.
It’s an arms race of annoyance. Now we have "AI Inbox Filters" to block the "AI Sales Bots." We’ve created a digital ecosystem where the only things talking to each other are scripts, while the humans are just trying to find an actual message from their mom.
Hilbert: And the "confidence" of these models is the problem. They’ll cite fake case studies or "hallucinate" that your company has a problem it doesn't have, just to get a meeting. It’s professionalized lying at scale. I once got a pitch from an AI SDR claiming they had worked with "Corn and Herman" on a project that never existed. It’s reaching a level of delusion that is actually impressive.
It’s also incredibly cheap to run. That’s the danger. It costs a fraction of a cent to send ten thousand of these emails. Even if the conversion rate is 0.0001%, the ROI is there for the spammer, but the "social cost" of a destroyed inbox is paid by all of us.
It’s exhausting. Alright, what’s number three? We’re getting into the heavy hitters.
Hilbert: Number three: The CRM Platform "AI Tone Polisher." Most enterprise office suites now have a feature that will take a simple, human email like "Thanks for the update!" and "polish" it into a three-paragraph corporate masterpiece. It adds "professionalism" by removing all traces of human personality.
This is the death of authenticity. Technically, these are just using a "style transfer" prompt. But the result is this weird, uncanny valley of corporate-speak. If I get an email that says, "I would like to express my sincere gratitude for the timely and comprehensive update you provided regarding our ongoing initiatives," I know for a fact you didn't write it. And because I know you didn't write it, the "gratitude" feels fake.
It’s "AI-polishing" the soul right out of the room. I’d much rather get a "Thanks!" than a generated essay. It’s like we’re afraid of being human at work, so we use a machine to pretend to be a more "efficient" version of ourselves. It’s embarrassing.
Hilbert: But what about the cultural impact? If you’re a non-native speaker and you use these tools to "fit in," you’re essentially being told that your natural voice isn't professional enough. It’s a subtle form of linguistic erasure. We’re standardizing everyone to sound like a mid-level manager from a 1990s insurance firm.
It’s also a security risk. If everyone’s writing style becomes "Standard AI Corporate," it becomes much easier for phishers to mimic internal communications. We’re losing our unique "linguistic fingerprints."
That’s a great point. Diversity of thought and style is actually a security feature. We’re trading it for "polish." Okay, number two.
Hilbert: Number two: Project management tools that use an LLM to "predict" project delays based on task descriptions. It looks at a ticket that says "Update CSS for landing page" and tells the manager, "This task has a forty-two percent chance of being delayed by three days."
This is pure "statistical theater." LLMs are not predictive engines for complex human workflows; they are linguistic engines. They don't know that the developer assigned to that CSS task is currently moving house or that the server is acting up. They’re just looking at patterns in the text of the ticket.
It’s "Vibes-based Forecasting." The AI says it’s going to be late because it’s seen other tickets with the word "CSS" be late. So now you have a manager breathing down your neck based on a "prediction" that has no basis in reality. It creates unnecessary stress based on a hallucination of probability.
Hilbert: It’s the "Black Box" problem in a business-critical system. No one can explain why the AI thinks it’ll be late, so everyone just treats it as "The Truth" because it came from a computer. It’s the opposite of data-driven; it’s "slop-driven."
I saw a case study where a team started naming their tasks "Super Easy Fast Task" just to trick the AI into giving them a better "on-time" score. If the AI is judging you based on keywords, you just change the keywords. It’s a total farce.
"Slop-driven." We should put that on a t-shirt. Alright, Hilbert. We are at the summit. What is the number one most absurd, unnecessary, and frankly insulting AI feature of the 2026 era?
Hilbert: Number one: AI "Social Presence" Mimicry. These are apps that promise to "post as you" on social media while you sleep or work to maintain your "algorithm heat." It essentially creates a digital ghost of yourself to keep the engagement numbers up while you’re busy actually living your life.
This is the logical conclusion of everything we’ve talked about. It’s the complete outsourcing of the self. From a technical perspective, it’s just a fine-tuned model on your past posts, but ethically and socially, it’s a nightmare. It led to the "Authenticity Movement" we’re seeing in 2026, where people are literally "Human-Certifying" their social media accounts.
It’s the "uncanny valley" of friendship. Imagine thinking you’re having a deep conversation with a friend in a comment section, only to find out it was their "Social Mimicry Bot" keeping their "algorithm heat" alive. It makes every interaction suspect. It turns the internet into a literal ghost town.
Hilbert: And it’s unnecessary! Why do we need "algorithm heat" if the heat is being generated by and for other bots? We’ve built a perpetual motion machine of uselessness. I’ve seen influencers who haven't logged into their own accounts in six months, yet their "presence" is more active than ever. It’s a digital taxidermy of the soul.
It’s interesting, because if you look at why these things exist, it always comes back to two things: marketing and monetization. If you’re a hardware company making a toaster, you can only sell that toaster once. But if you add "AI Toast Optimization" as a service, you can charge five dollars a month forever. AI is being used as a Trojan horse to turn everything into a subscription.
Hilbert: Exactly—oops, I almost said the "E" word. Herman would have killed me. But you're right. It’s a "Subscription Trap." They’re not adding AI to help you; they’re adding AI to help their quarterly recurring revenue.
And for the developers out there, there's this "AI Feature Litmus Test" I’ve been thinking about. If you can't explain the model's input and output in one sentence without using the word "smart," it’s probably unnecessary. "The model takes a picture of milk and tells you it's milk." See? Sounds stupid immediately.
Another one is the "Friction Test." Does this AI feature add more steps than the manual task it’s replacing? Correcting an AI summary of a two-minute meeting takes longer than the meeting itself. That’s a fail.
Hilbert: But what about the "Fun Fact" of the day? Did you know that in 2025, a startup tried to launch an AI-powered "Smart Pillow" that would analyze your dreams and tweet them for you? It was called "DreamStream." It failed because, shockingly, most people's dreams are just incoherent nonsense about being back in high school without pants.
See, that’s where I draw the line. My subconscious is private property. Keep the LLMs out of my REM cycle.
So, what do we do? How do we resist the "AI Stuffing"?
Hilbert: We stop buying it. We start looking for "Human-Certified" products. I’ve seen companies now marketing products based on the absence of AI. "Our blender is just a blender. It has a motor and a blade. No Wi-Fi, no neural networks, no nonsense." People are starving for that.
It’s about being an intentional consumer. Audit your own software stack. If you’re paying for a CRM because of its "AI insights" but you never actually look at them because they’re useless, cancel it. Demand utility over novelty.
And for the love of all that is holy, if your toothbrush tries to tell you you're sad, just ignore it. You're allowed to be sad while you brush your teeth. It’s a very reasonable time to reflect on the state of the world.
I think we’re going to see a "Great Simplification" soon. The companies that survive the next couple of years won't be the ones that stuffed AI into everything; they’ll be the ones that used AI to solve one really hard, really annoying problem so well that you forget the AI is even there.
Like a toaster that just... toasts bread perfectly without needing to know my acoustic preferences.
Hilbert: One can dream, Corn. One can dream.
Well, this has been an enlightening—and slightly depressing—countdown. Hilbert, thanks for stepping out from behind the desk. Your cynicism is a breath of fresh, non-hallucinated air.
Hilbert: Don't get used to it. I have a mixing board to go yell at.
Fair enough. Big thanks to Modal for providing the GPU credits that power the actual, useful parts of this show's pipeline.
And thanks as always to our producer, Hilbert Flumingtop—glad you could join the conversation today, Hilbert.
If you’ve encountered a truly absurd piece of AI bloat, we want to hear about it. Search for "My Weird Prompts" on Telegram and share your stories. We might just do a "Listener's Choice" version of this list.
This has been My Weird Prompts. Find us at myweirdprompts dot com for the full archive.
Stay human, everyone. Or at least, stay more human than your grocery bag.
Goodbye.
See ya.
