Your Wearable's AI Is Broken. Here's What Replaces It.

I've worn a Garmin watch almost every day for almosst 10 years. My Enduro 3 tracks my runs, my sleep, my stress, my heart rate variability — it knows more about my body than my doctor does. And yet, when I open Garmin Connect and tap on their new AI-powered "Active Intelligence" feature, I get advice like: "You slept 7 hours and 12 minutes. Try to maintain consistent sleep habits."

That's it. That's the insight I'm supposed to pay $6.99 a month for.

I've worn a Garmin watch almost every day for the past four years. My Enduro 3 tracks my runs, my sleep, my stress, my heart rate variability — it knows more about my body than my doctor does. And yet, when I open Garmin Connect and tap on their new AI-powered "Active Intelligence" feature, I get advice like:

This used to be fine. For years, wearable companies like Garmin, Whoop, and others could rest on the laurels of their hardware. They had the sensors, the algorithms, the user base — if they wanted to deliver personalized health insights, they were in the best position to do it.

But something has shifted. In the last year, every major AI lab has launched health-focused features. Google's Gemini can now read your Apple Health data. Anthropic's Claude can analyze your health metrics. Meanwhile, OpenAI just launched ChatGPT Health. Anthropic's Claude can now read Apple Health data natively. Open-source MCP servers are connecting 500+ wearable devices to any AI model you choose. The entire health-tech landscape is shifting beneath our feet — and the wearable companies that built these incredible sensors are at risk of becoming the dumbest link in their own chain.

This is the story of where wearable health AI stands in February 2026, why the current approach is broken, and what a genuinely useful personal health assistant could actually look like.

TL;DR: Wearable companies like Garmin are charging subscriptions for AI features that barely work, while open platforms like MCP + Claude and ChatGPT Health are enabling genuinely personalized health assistants that can reason across all your data. The future isn't a closed app with canned advice — it's an AI agent that understands your body, your goals, and your life context. We're closer to that future than most people realize.

What's Actually Happening with Wearable AI in 2026

Let me lay out the landscape as it exists right now.

Garmin's Active Intelligence

Garmin Connect+ is the most ubiquitous wearable platform with AI features. For $6.99/month, you get "Active Intelligence" — a feature that supposedly analyzes your body battery, sleep, training load, and heart rate variability to give you personalized coaching.

In practice, what you actually get is:

• "You have elevated stress. Consider a rest day."

• "Your resting heart rate is slightly elevated. Ensure you're hydrated."

• "You slept 7.5 hours. Maintain your current sleep habits."

These are all real-world examples. They're generic, surface-level, and could apply to literally anyone. They don't account for my specific goals, my training philosophy, my life context, or any of the nuanced data that makes health advice actually useful. They're not bad because Garmin's engineers are incompetent — they're bad because this approach to AI is fundamentally limited.

Garmin's approach to AI is template-based. They've got a set of rules and thresholds, and the "AI" is just matching your data against those templates. It's not learning. It's not reasoning. It's not doing anything that resembles actual intelligence.

Whoop's Membership Model

Whoop takes a different approach. Their strap costs $30 and includes a $30/month membership. In exchange, you get:

• Recovery scores (based on HRV, RHR, and sleep)

• Strain scores (based on activity and effort)

• "In-app coaching" that amounts to "Your recovery is low, get more sleep."

• The ability to export your data as a CSV (which is honestly the most useful feature)

  
  

Whoop's main innovation is their proprietary recovery and strain algorithms — they're genuinely better at predicting your readiness than most competitors. But their "AI" is just scorecards. They're not providing reasoning, context, or personalization. They're not asking about your goals, your schedule, or your life. They're just giving you numbers.

Google Fitbit and Suunto's Approach

Google acquired Fitbit and has been quietly integrating AI features through Gemini. You can now ask Gemini questions about your health data, and it will try to provide insights.

This is genuinely more useful than Garmin's approach, but it's still limited by Google's privacy-first approach. You're not getting personalized coaching because Google doesn't want to store your detailed health history in a way that could be used for more targeted advertising.

Suunto and Amazfit have taken a similar path — they've added basic AI features through partnerships with larger AI companies, but again, these are limited, template-based insights.

Withings' Approach

Withings is taking a different approach. Instead of building AI features directly, they're focusing on making their devices work with Apple Health and third-party health AI apps. This is actually a smart move — it acknowledges that the future isn't about owning the entire health stack, it's about owning the best sensors and letting AI platforms do what they're good at.

But even Withings' approach is limited because most third-party health AI apps are still using template-based approaches.

Why This Approach Is Broken

Let me be clear: the hardware is amazing. The sensors in modern wearables are genuinely impressive. The issue is what happens with the data.

The problem is that health data is deeply personal and context-dependent. A resting heart rate of 52 bpm is excellent for a trained athlete but could indicate overtraining, illness, or medication effects for someone else. Sleep timing that works perfectly for someone in San Francisco might create jet lag problems for someone constantly traveling. A stress spike that means "I need a rest day" for an endurance athlete might mean "I just got bad news and should prioritize recovery in a different way" for someone else.

Good health advice requires:

• Understanding your specific goals (losing weight, training for a marathon, managing chronic illness, improving sleep quality)

• Knowing your life context (work schedule, travel, stress level, diet, medications)

• Reasoning across multiple data sources (wearables, medical records, nutrition, mental health, environmental factors)

• Adapting to your preferences and constraints (you might be willing to take medication but not change your sleep schedule)

• Actually understanding you're a human being with competing priorities (weight loss is great, but not at the cost of your mental health or social life)

Template-based systems can't do any of this. They're designed to be one-size-fits-all, which means they fit nobody well.

What's Actually Changing: MCP and Claude

So here's the thing: the future of wearable health AI isn't coming from Garmin, Whoop, or even Fitbit. It's coming from something completely different — a new class of tools that lets AI models like Claude connect to hundreds of data sources through an open standard called Model Context Protocol (MCP).

MCP is an open-source protocol that lets any AI model connect to any data source. Think of it like USB for AI — once you have it, devices that support it just work together.

What this means for health is pretty revolutionary. Instead of being stuck with your wearable company's proprietary AI, you can feed your health data to Claude (or any other capable AI model) and ask it to:

• Analyze your wearable data alongside your Apple Health data, your nutrition tracking, your calendar, and your personal notes

• Reason about what your data means in the context of your specific goals and life situation

• Ask follow-up questions and adapt its advice based on your responses

• Integrate with hundreds of other tools and data sources you already use

This is genuinely a different category of system than what wearable companies are building.

Here's What It Could Actually Look Like

Let me paint a picture of what a real personal health AI assistant could do — and in many cases, can already do right now:

You wake up on a Monday with elevated heart rate variability. Your Garmin watch notes it. Most systems stop there. "Your HRV is low, get more sleep."

But a real AI assistant, fed with MCP access to all your data, could do this:

• Notice your HRV is low, but also see that you had 7 hours of deep sleep (which is good) and your RHR hasn't increased (which is important)

• Check your calendar and see you have a big presentation today

• Look at your historical data and see that your HRV often dips the day before stressful events

• Check your training plan and see that today is supposed to be a rest day anyway

• Know that you mentioned wanting to improve your presentation skills

• Instead of telling you to sleep more, it tells you: "Your body is signaling pre-event stress. This is normal and your sleep quality was actually good. I'd recommend sticking to your rest day plan, doing some light yoga to calm your nervous system, and having a good breakfast. Your HRV typically recovers by afternoon, and being well-rested will actually help your presentation more than pushing a workout would."

That's not a template. That's reasoning. That's understanding context. That's useful.

Why This Matters

The significance of MCP and open AI models goes beyond just better health advice.

For users, it means:

• You're not locked into your wearable company's ecosystem

• You control your own data

• You can use the best AI model available, not whatever your hardware company decided to integrate

• You get genuinely personalized advice, not template-based platitudes

• Your AI assistant can reason across all your data, not just the data your wearable company owns

For wearable companies, this is actually an opportunity if they get it right:

• Build the best sensors, not the best AI

• Make your data easily accessible to any AI platform the user wants to use

• Stop charging subscription fees for mediocre template-based features

• Partner with AI companies instead of competing with them

The Bottom Line

The wearable companies have spent the last decade building the best health sensors in the world. They've earned their place at the center of personal health tech. But the moment they tried to add AI, they made a crucial mistake: they assumed that owning the sensors meant owning the AI.

They were wrong. The AI is what matters now. And it's not coming from inside closed ecosystems — it's coming from open platforms, open models, and open protocols like MCP.

If you've got a Garmin, a Whoop, or any other wearable gathering dust while you ask Claude or ChatGPT about your health, congratulations — you're already in the future. The wearable companies are still trying to figure out how to catch up.