Smart glasses are in a weird place right now. While a chunk of the population views them as cool and innovative, another significant chunk sees them as a privacy nightmare. If you’re in the latter group but want to see what life is like on the former, DuckDuckGo has a new product: a pair of anti-surveillance glasses with no camera or AI, otherwise known as just normal sunglasses.

If it’s not obvious, yes, it’s a troll product. DuckDuckGo is a search engine famously known for pushing privacy as a priority, opposing Google’s penchant for advertising and AI. Now, the search engine is taking on the smart glasses market.

Partnering with Knockaround, DuckDuckGo released the Paso Robles, a pair of sunglasses touted as having zero cameras, an always-offline mode, and a battery life of infinity.

It’s a statement piece. The rims have a logo mark, similar to the Meta’s glasses. The packaging also states that the glasses are “designed to block the sun, not send data to the cloud.”

Now, if you want a pair, they cost only US$ 35, as opposed to Meta’s US$ 299 price tag, but they are already sold out.

Which says quite a bit about today’s sentiment against AI and for privacy. Of course, to be fair, DuckDuckGo’s supply is certainly less than Meta’s stock for their smart glasses. It does, however, prove that the sentiment is there.

As of now, the world is slowly realizing that unimpeded smart glasses aren’t such a good idea, leading to people concerned about their privacy. In fact, some states have already started banning the technology in certain public spaces.

SEE ALSO: New York becomes first state to ban smart glasses

Step on a scale at a doctor’s office and your health usually gets summed up in one number. Weight divided by height squared. That’s your BMI, and it’s been the default health metric for almost 200 years.

It was never built for that job. BMI was invented in the 1830s as a statistical shortcut for studying populations, not for telling an individual person anything meaningful about their body. It can’t tell the difference between a muscular athlete and someone who’s out of shape but happens to weigh the same. It ignores bone density, body water, and where fat actually sits in the body, which matters more than most people realize.

The metrics that actually matter are the ones BMI was never designed to catch: how much of your body is muscle, how much is fat, and where that fat is sitting.

The muscle question BMI can’t answer

Skeletal muscle is the body’s metabolic engine. It’s what keeps your resting metabolic rate up, what protects your joints, and what determines how well you move as you get older. Losing it quietly, without knowing, is one of the bigger blind spots in how people track their own health.

Fat isn’t one thing either. Subcutaneous fat, the kind just under the skin, carries far less risk than visceral fat, which wraps around internal organs and is directly linked to cardiovascular problems. A person can lose weight and still be carrying dangerous levels of visceral fat. BMI has no way of showing that.

This is also why short-term weight drops can be misleading. A lot of what shows up on the scale in the first week or two of any diet is water loss, not real change in body composition. The number moves, but it doesn’t mean much yet.

Why this is showing up in headlines right now?

Part of why this conversation is happening now, rather than five years ago, comes down to the rise of GLP-1 weight-loss drugs like Ozempic, Wegovy, and Zepbound.

GLP-1, or glucagon-like peptide-1, is a natural gut hormone that signals fullness to the brain. These medications mimic it, slowing digestion and dulling appetite, which lets people eat significantly less and lose weight fast.

That speed is exactly what exposed the problem. Rapid weight loss burns fat and muscle together, not just fat. Lose 20 pounds on a GLP-1 drug, and both the scale and a BMI calculator will call it a clean win.

If a meaningful chunk of that was skeletal muscle, metabolic rate drops, joint support weakens, and the risk of physical frailty goes up, none of which shows up in that one number.

Dr. Hon Pak, Head of Digital Health Globally at Samsung Electronics, has pointed out that with GLP-1 therapies in the picture, raw weight lost can’t be the metric that defines success anymore. The focus has to shift to what kind of weight is being lost.

Kevin Duffy, CEO of iFit, has made a similar point: BMI stuck around because it’s easy to calculate from just height and weight, not because it’s the right thing to track. Muscle mass and body fat percentage matter far more, they’ve just historically been hard to measure outside a lab.

What your watch can measure now

Getting real numbers on muscle mass and visceral fat used to mean an expensive clinical scan. That’s the part that’s changed.

Samsung has built Bioelectrical Impedance Analysis sensors into the Galaxy Watch, sending a low-level microcurrent through the body to estimate skeletal muscle mass, body fat percentage, body water, and basal metabolic rate, all from your wrist.

For anyone losing weight, whether through a GLP-1 medication or a more traditional route, that gives real visibility into whether the weight coming off is fat or muscle.

It’s the kind of feedback that lets someone actually adjust, adding resistance training or upping protein intake, instead of finding out the hard way months later.

Samsung pairs this with Samsung Food, which uses AI to look at what’s actually in someone’s kitchen and suggest protein-forward meals that support muscle retention.

Karthik Poriya, Head of Product at Samsung Food, has noted that people tend to fixate on small day-to-day food decisions when what actually moves the needle is the trend over weeks and months. AI is what makes it possible to zoom out and see that trend instead of getting lost in it.

Combine body composition data with sleep tracking, activity monitoring, and a broader Energy Score, and the whole framework shifts. It’s no longer about getting lighter.

It’s about getting stronger, protecting muscle, and extending healthspan rather than chasing a number that was never built to measure any of that in the first place.

Most of the conversation around artificial intelligence right now revolves around chatbots. Tools that write your emails, summarize long PDFs, or spit out blocks of code in seconds. That is the AI most people interact with daily.

Behind the scenes in health research labs, however, a profound shift is taking place that has nothing to do with words.

Scientists are training AI models to read raw human biology instead: optical heart rate signals, ECG readings, movement patterns, and sleep data. The goal is teaching AI to understand what is happening inside your body long before you ever feel sick.

During Samsung’s panel discussion on the future of Connected Health at the Pan Pacific London, Otavio Penatti, Samsung’s Head of Health AI R&D, captured this vision simply: “Your body speaks, Samsung Health translates.”

So what makes this different from your average fitness app?

Most health apps you already use run on basic rules. Your heart rate crosses a certain number while you’re resting, so the app sends an alert. That’s about as sophisticated as it gets.

These new models work on a different level entirely. Researchers feed them massive amounts of raw biometric data without labeling any of it first.

No doctor has to sit there and tag every second of heart rate or movement data by hand. The AI figures out the patterns on its own, essentially learning the basic language of how a human body functions.

Once a model has been trained on millions of hours of this kind of data, it can then be fine-tuned for a long list of specific medical uses.

And it’s not just watching for one odd spike in your heart rate. It’s cross-referencing your heart rate variability, skin temperature, movement, and sleep patterns all at once to build a detailed picture of what’s normal for you specifically.

One night of sleep data, over 130 diseases

Here’s where it gets interesting. Researchers at Stanford built a foundation model trained specifically on sleep data, using both raw polysomnography (the kind of detailed sleep study done in a lab) and data from wearables.

When they compared just 24 hours of sleep data against years of longitudinal health records, the model was able to flag onset risk for more than 130 different diseases. From one night of sleep.

Dr. Hon Pak, who leads Digital Health globally at Samsung, described where this is headed: “We are collecting data right now in which we have clear signals of things to come. We just don’t know what it is right now. But through the work that we’re doing, we’ll be able to share these insights early and in the right, meaningful way.”

The missing link: your wearable talking to your medical chart

Right now, your Apple Watch or Galaxy Watch has no idea what your last blood test looked like. Your wearable data and your actual medical records live in two completely separate worlds.

That’s the real opportunity here. If continuous biosignal data from your wearable gets linked with your actual health records, prescription history, and lab results, AI could start catching subtle shifts in your body that even your doctor might miss during a routine checkup.

Not because your doctor isn’t paying attention, but because a once-a-year appointment simply can’t compete with data collected every single day.

Instead of catching a heart attack or a metabolic crisis after it’s already happened, the idea is to catch the early warning signs years before that. It’s a shift from reacting to illness to catching it early enough that it might never become a crisis at all.

It’s worth noting that most of this is still in research stages. How it eventually shows up in the health apps on your phone will depend a lot on how these companies handle the data, and how accurate these predictions actually turn out to be in practice.

But the direction is clear enough: the next major leap in AI might not be about how well it writes. It might be about how well it listens to you.