AI Hardware Hype Is a Lie. Users Don’t Buy Algorithms.

Every tech founder knows the painful realization of “having a hammer and looking for a nail.” You see a shiny new architecture, a breakthrough model, and immediately scramble to find a use case for it.

But in the world of AI hardware, this approach is a death sentence.

Large model capabilities have undeniably emerged. The tech works. But tech viability does not equal product viability. Between a working algorithm and a successful product lies a massive chasm filled with cost constraints, context limits, and safety liabilities.

If a user takes your device home and can’t find a stable use case, if the price, value, and experience don’t close the loop, your “innovative interaction” just becomes an expectation gap.

Sun Hao, a 12-year veteran at 360 who now helms AI hardware at Xiaodu, has lived through this cycle. He oversaw smartwatches, speakers, and cameras. And after navigating the AI boom, his conclusion is ruthless: the hardware hype is repeating the same old mistakes.

Here is the sober roadmap of what actually matters when building AI products.

The Boring Product That Won

If you look at the AI hardware space right now, the product category that actually makes commercial sense isn’t a futuristic projector or AR glasses. It’s the humble recording card.

Why? Not because the hardware is impressive, but because it plugged a gaping hole in an existing workflow.

Before AI, recorders captured sound. ASR let you read transcripts. But a human still had to sit down, sift through the text, summarize it, and send it out. The job wasn’t finished.

The large model didn’t upgrade the hardware; it completed the workflow. Now, minutes after a meeting, a summary is generated and shared. Recording was never about recording. It was the prerequisite for sharing, expressing, and summarizing.

The 360 AI Note wasn’t built to summarize a single meeting. It was designed as the entry point for the entire office context—pulling pre-meeting materials from a knowledge base, recording the discussion, and spitting out post-meeting emails and slide decks.

It didn’t sell a feature. It sold a completed task.

The Context Paradox

Once you see the power of context, the instinct is to capture everything. Why just record audio when a camera can intermittently log your day, remember who you met, and track what you looked at?

This is the most dangerous trap in AI product development.

Theoretically, richer context yields more AI value. Practically, it destroys hardware viability.

The Context Paradox: richer context means better AI, but it destroys hardware viability through exponential increases in power consumption, inference costs, and latency.

Capture video every 10 seconds, and you drain the battery while racking up massive cloud inference costs just to vectorize and process low-density information. Capture every 5 minutes, and the battery survives, but you miss the critical moment. The subscription fee won’t cover the compute.

This is why vertical scenarios beat general imaginations. Instead of building a generic life-logger for all professionals, shrink to a provable scenario. Run one specific workflow perfectly before trying to boil the ocean.

You Cannot Monetize an Algorithm

Back in 2019, Sun Hao’s team built a high-end smart camera. They loaded it with edge AI chips and envisioned an “app store for algorithms.” Users could buy different recognition models for their cameras.

It failed miserably.

Trying to sell an algorithm directly to a user is like trying to sell them a chunk of raw iron. Users do not buy algorithms; they buy scenarios.

The breakthrough came when they stopped selling “AI detection” and started bundling it with traditional cloud recording. A user won’t pay for an algorithm, and they won’t pay $100 a year for a cloud tape when a $10 SD card works fine. But they will pay for peace of mind: knowing if their car gets keyed, if a car blocks their store entrance, or if a child picks up a dangerous tool.

By bundling AI capabilities into a concrete, understandable service, the abstract becomes purchasable.

The Unseen Burden: Safety and Speed

When building for adults, a little latency is fine. When building for kids, it’s a matter of safety.

Sun Hao learned this building smartwatches for children. Kids don’t care about long-term memory or model parameters. They want a companion. Parents want a safe, educational tutor.

But children haven’t built a worldview. If an LLM hallucinates and confidently tells a child a lie, the child will believe it. To counter this, the team had to bolt on heavy RAG knowledge bases, force deterministic answers, and implement strict content filtering.

Then came the latency debate. Do you stream the output instantly, or wait, filter it, and then speak? They chose caution. The competitor’s watch responded in 1 second. Theirs took 3.

Neutrality is death in product strategy, but when it comes to child safety, neutrality is a liability. You must sacrifice speed for absolute security.

The capability had emerged, but placing it into a real-world constraint required massive, painful克制 (restraint).

The Sober Reality

Capabilities have emerged. Hardware is getting cheaper. Edge compute is dropping in price. But a successful product is not an automatic byproduct of these trends.

It has to fall into a specific demographic, a specific scenario, a specific cost structure, and a specific value expression.

Stop chasing shiny new form factors. Stop falling in love with your architecture. Find the workflow, close the loop, and make the value concrete. That is the only way AI hardware survives.