Why the AI Hardware Revolution is Repeating the IoT’s Biggest Mistakes

There is a pattern the tech industry keeps repeating. A product ships with sensors, connectivity, and software layered on top of ordinary hardware. It is labeled “smart.” Expectations spike. A year later, it quietly disappears.

This is not an AI problem. It predates generative models by a decade. The real issue is that intelligence is often added where it does not create lasting value, and complexity is mistaken for progress.

The most recent example just happens to involve AI.

Case Study: The “Humane” AI Pin

The Humane AI Pin was marketed as a post-smartphone device: a wearable, screenless assistant meant to handle daily tasks through voice and context. It was ambitious, thoughtfully designed, and genuinely novel, the perfect product for an up-and-coming startup.

It also ran into the same wall many “smart” devices hit.

Early reviews described reliability problems: slow responses, inconsistent behavior, and overheating during normal use. The laser-projected hand interface looked futuristic but proved awkward in everyday conditions. Battery life reportedly fell short of a full day, requiring spare batteries and a charging case to make the product usable.

More importantly, the device struggled to justify its existence next to a modern smartphone that was already absorbing similar AI features. Without a clear, sustained advantage, it became an accessory searching for a purpose.

According to public reporting, Humane later sold its intellectual property to HP, and the AI Pin hardware is expected to stop functioning once backend services are shut down. If accurate, that outcome also highlights a recurring risk with cloud-dependent devices: when the service ends, the product ends with it.

Humane is not an outlier. It is the latest chapter.

Before AI: The “Smart” Label Problem

Long before AI companions and contextual wearables, the industry was already experimenting with intelligence in places it did not belong.

A frequently cited example is Juicero, a connected juicer that required proprietary juice packs and an internet connection to operate. It promised optimization and precision. It became famous for a simpler reason: people discovered they could squeeze the juice packs by hand. The “smart” part added cost, friction, and failure points without improving the outcome.

Smart refrigerators that reordered groceries few people wanted automatically. Smart toasters controlled by mobile apps. Smart water bottles that tracked hydration but required constant charging. Smart lightbulbs that stopped working when a cloud service went offline.

Different categories, same result.

The Common Failure Modes

Across these products, several patterns repeat with remarkable consistency.

First, intelligence is added without necessity. The core function works perfectly well without software, connectivity, or data collection. The added layer does not make the experience meaningfully better, just more fragile.

Second, reliability drops as complexity rises. Sensors drift. Apps crash. Firmware updates break workflows. When a basic function like turning on, squeezing juice, or recording a note fails, users lose patience quickly.

Third, the business model leaks into the product. Subscription requirements, locked ecosystems, and server dependencies turn physical objects into temporary licenses. When companies pivot, get acquired, or shut down, the hardware becomes inert.

Fourth, integration is underestimated. Smartphones already act as hubs for identity, connectivity, and computation. Any standalone device has to either integrate seamlessly or offer a clear, lasting advantage. Most do neither.

AI-first devices simply inherit these problems, with a new layer of uncertainty added on top.

AI Companions Are Not Immune

Products like the Rabbit R1 and similar AI-centric gadgets show how quickly novelty fades when the underlying experience is inconsistent. Bugs, latency, and unclear value propositions matter more than ambition.

Privacy concerns also scale with intelligence. Devices that record audio, context, or behavior raise questions not just about the user, but about everyone around them. Transparency helps, but it does not eliminate discomfort once recording becomes ambient.

There is also the issue of agency. When devices promise to remember, decide, or summarize on behalf of users, they need to be dependable. When they are not, users are left unsure whether to trust the system or themselves.

A More Modest Direction

Some companies appear to be absorbing these lessons, at least partially. Lower-cost, accessory-style devices that avoid cameras, limit scope, and position themselves as optional rather than essential are starting to appear. One example discussed in industry coverage is Bee, an audio-only journaling device reportedly showcased by Amazon.

Whether that approach succeeds remains uncertain. But the shift in framing is telling.

The history of failed “smart” products suggests a simple rule. Intelligence does not create value on its own. Reliability does. Clear purpose does. Respect for existing habits does.

Most smart devices fail after the first year not because the technology is immature, but because users eventually ask a basic question and do not like the answer: what problem does this solve better than what I already have?