What if your hearable could listen to a live conversation all day, on one charge?

What if your hearable could listen to a live conversation all day, on one charge? That kind of always-on, low-power processing is moving from research into real products in 2026.

The technology making it possible is called neuromorphic computing - an approach that borrows ideas from how the brain works, rather than how traditional computer chips work.

Here's the difference. Most conventional computing architectures spend significant energy moving data between memory and compute, reading it, processing it, storing it, and moving it again. That data movement can become a major part of the energy cost. It's one reason energy efficiency is becoming a much bigger issue as AI workloads grow.

The human brain runs on roughly 20 watts while performing extraordinarily complex, highly parallel computation. Neuromorphic systems borrow one important idea from biology: compute when useful information arrives. When there is little or no relevant activity, much of the computation can stay inactive.

𝘞𝘩𝘦𝘳𝘦 𝘪𝘵'𝘴 𝘢𝘭𝘳𝘦𝘢𝘥𝘺 𝘣𝘦𝘪𝘯𝘨 𝘶𝘴𝘦𝘥:
→ Innatera makes the Pulsar chip, already commercially deployed for always-on sensing, including audio and motion-related applications. It processes sensor data locally without relying on the cloud.

→ 42 Technology is applying the same approach to industrial condition monitoring - using neuromorphic processing to detect faults such as imbalance or misalignment in motors, fans, and pumps before they cause costly downtime.

These are real-world commercial applications, currently concentrated at the small, battery-powered edge. Intel and IBM are testing much bigger versions of this idea in research - showing that the approach can scale beyond tiny edge devices, but not yet as something companies can simply buy for their data centres.

𝘈 𝘴𝘪𝘮𝘱𝘭𝘦 𝘸𝘢𝘺 𝘵𝘰 𝘵𝘩𝘪𝘯𝘬 𝘢𝘣𝘰𝘶𝘵 𝘸𝘩𝘦𝘳𝘦 𝘵𝘩𝘪𝘴 𝘨𝘰𝘦𝘴 𝘯𝘦𝘹𝘵:
Now - 2028: Small, battery-powered devices. Wearables, hearables, industrial sensors. Already happening.

2028 – 2031: These chips could start appearing alongside conventional processors in products such as cars and robots, handling the "always-on, low-power" parts while the main processor handles heavier workloads.

2031 and beyond: If the software ecosystem catches up, neuromorphic computing could reach data centres and enterprise AI for certain workloads. Not guaranteed - but the direction is attracting serious investment.

Also worth noting from a talent lens: the pool of engineers who understand neuromorphic hardware, spiking networks, and edge deployment is still tiny compared with conventional AI and software engineering - a gap worth watching if your roadmap touches robotics, wearables, or anything battery-constrained.

At iForce Connect, we're already fielding early conversations about this kind of specialist hiring - happy to compare notes if you're thinking about it too.