RobotWorld

Why Also Chose Autonomous Vehicles Over E-Motos — And What It Reveals About the AV Opportunity

By RobotWorld·11/4/2026

A startup that began life inside Rivian — the electric vehicle maker best known for its pickup trucks and SUVs — recently made a sharp strategic turn. Also, co-founded by Chris Yu, had been exploring an electric motorcycle project aimed at markets like Kenya, where two- and three-wheelers are a primary mode of transportation and commercial logistics. That project has now been set aside. According to Yu, the reason is straightforward: autonomous vehicles are simply the bigger opportunity, and Also wants to focus its energy there.

It's a decision that might seem surprising at first glance — after all, electric mobility in high-growth African markets is a genuinely compelling space. But when you understand the technical and commercial forces shaping the AV industry right now, Also's pivot starts to make a lot of sense.

The Autonomous Vehicle Moment

Self-driving technology has been in perpetual "almost ready" mode for most of the past decade. But the landscape has shifted meaningfully in recent years. Computing hardware has become dramatically more capable and more compact. Sensor technology — particularly LiDAR — has matured from expensive, spinning research instruments into compact, production-grade systems. And the software stacks powering autonomous perception and decision-making have improved in ways that were difficult to predict just a few years ago.

The result is that autonomous vehicle development now feels less like a research moonshot and more like a solvable engineering challenge — at least in constrained, well-mapped environments like logistics hubs, campuses, and designated urban corridors.

This is the moment Also appears to be betting on. By concentrating its resources on AV development now, the company is positioning itself to participate in what many analysts expect will be a significant commercial expansion of autonomous transport in the coming years.

Perception Hardware: The Backbone of Any AV

One of the most consequential decisions any AV developer makes is how to build its sensor stack. Modern autonomous vehicles rely on a layered combination of cameras, radar, and LiDAR to construct an accurate, real-time picture of their surroundings.

LiDAR, in particular, has become a defining technology. Sensors like the Hesai AT128 — a 128-channel hybrid solid-state unit certified to automotive-grade safety standards — are now deployed on production electric vehicles and robotaxi fleets, illustrating just how far the technology has come from its early prototype-only days. Similarly, the RoboSense RS-LiDAR-M1 is a solid-state MEMS sensor designed and validated specifically for series-production ADAS and autonomous vehicle applications, offering a substantial detection range in a compact, durable package.

These aren't research curiosities anymore. They are mass-manufactured, safety-certified components that are being integrated into vehicles shipping to real customers.

The Compute Side of the Equation

Sensor data is only as useful as the compute power available to process it. The amount of raw information streaming in from cameras, LiDAR units, and radar at any given moment is immense, and processing it fast enough to make real-time driving decisions demands serious hardware.

Platforms like the NVIDIA DRIVE AGX Thor — built on Blackwell GPU architecture — are purpose-built to handle this challenge, unifying autonomous driving, cockpit, and infotainment functions on a single compute platform capable of delivering thousands of TOPS (tera-operations per second). For companies developing AV systems at a production scale, this kind of centralized, high-throughput compute is increasingly the standard to design around.

At a slightly different tier, the NVIDIA DRIVE AGX Orin handles perception and decision-making for L2+ through L4 autonomy levels, while chipsets like the Mobileye EyeQ6 High deliver high-performance ADAS and partial autonomy capabilities on a 7nm process — illustrating that the AV compute market now spans a wide range of capability and price points, from advanced driver assistance all the way up to full self-driving.

For developers working closer to the edge — on prototypes, research platforms, or low-power autonomous systems — the NVIDIA Jetson AGX Orin 64GB offers up to 275 TOPS of inference in a compact module, making it a popular foundation for autonomous machine development outside the automotive industry proper.

Why the E-Moto Exit Isn't a Failure

It's worth pausing on what Also's decision to exit the Kenya e-moto project is not: it's not evidence that electric mobility in emerging markets is a bad idea. The underlying demand for affordable, clean transportation in high-density urban markets across Africa and Southeast Asia is real and growing. Several companies are building serious businesses in this space.

What it is is a resource allocation decision. Building hardware for an entirely new market — managing supply chains, local distribution, regulatory engagement, and customer support in a nascent category — is an enormously complex undertaking. Doing that and building autonomous vehicle technology simultaneously is likely beyond the bandwidth of a startup at Also's stage.

Yu's choice to draw a clear line and concentrate on AVs reflects a kind of strategic discipline that is genuinely rare in the startup world, where the temptation to chase adjacent opportunities is always present.

What This Means for the AV Ecosystem

Also's pivot adds another datapoint to a broader trend: the autonomous vehicle industry is attracting serious technical talent and well-resourced teams who believe the core technology problems are now solvable in ways they weren't even five years ago. Each team that makes this bet — and backs it up with product decisions rather than just pitch decks — strengthens the overall ecosystem.

For anyone working in robotics, logistics, smart manufacturing, or last-mile delivery, the maturation of AV technology is directly relevant. Autonomous ground vehicles are increasingly being used in controlled environments like warehouses, ports, and agricultural settings, with public road deployment gradually expanding as regulatory frameworks catch up.

Whether Also ultimately succeeds in its AV ambitions remains to be seen. But its decision to focus is a signal worth paying attention to — both for what it says about the company and for what it says about where the frontier of autonomous transport is moving.

Interested in exploring the sensor and compute platforms powering the next generation of autonomous systems? Browse our curated selection of LiDAR sensors and edge AI hardware to find the right foundation for your project.

References

This article was drafted with AI assistance and reviewed before publishing.

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