Also: E-Bike Startup Replaces Physical Pedals With Software Control

The buzz around a chainless electric bike that treats pedaling like electricity generation might sound like a niche tech story, but for anyone navigating the bike lanes along Austin’s Lady Bird Lake hike-and-bike trail or dodging traffic on South Congress during rush hour, the implications are suddenly very local. When a startup like Also—founded by former Rivian engineers—unveils a bike where your legs power a generator instead of a chain, and software dictates how that effort translates to motion, it’s not just about drivetrain innovation. It’s a signal that the software-defined vehicle revolution, which has been reshaping cars and trucks, is now pedaling firmly into the two-wheeled space, promising a ride where resistance, acceleration, and even the feel of the road are tuned by algorithms, not just metal and grease.

This isn’t theoretical tinkering. The Also TM-B, as detailed in reports from digital outlets like Digital Today and highlighted in patent filings such as KR20250008185A, removes the physical link between pedals and wheel entirely. Instead, torque sensors measure how hard you’re pedaling, converting that mechanical input into electrical energy via an integrated generator. That power—blended with battery output—feeds a rear hub motor, but crucially, the relationship between your pedaling force and the bike’s forward thrust is mediated in real time by software. Push hard on the pedals, and the system instantly increases generator resistance even as boosting motor output, mimicking a downshift for quick acceleration. Ease off, and it reduces resistance, simulating a higher gear for cruising. The result, as described in the sources, is a riding experience that feels dynamically adaptive—like an automatic transmission for your legs—without requiring the rider to think about gearing at all.

What makes this relevant to Austin’s streets is how it intersects with the city’s growing emphasis on active transportation and micro-mobility solutions. The Austin Transportation Department has long promoted cycling as a congestion remedy, investing in protected bike lanes and advocating for end-of-trip facilities. Yet, one persistent barrier remains: the physical effort and perceived complexity of traditional cycling, especially in Austin’s hillier neighborhoods like Hyde Park or around the University of Texas campus. A bike that software-can smooth out effort spikes—making a steep climb on Guadalupe Street feel as manageable as cruising along the flat Barton Creek Greenbelt—could lower that barrier significantly. It’s not about eliminating exertion, but about regulating it intelligently, much like how pedal-assist e-bikes already do, but with a more seamless, integrated feel that removes the cognitive load of shifting.

Beyond individual comfort, there are broader urban implications. If software-defined drivetrains become common in e-bikes, maintenance demands could shift. No chain means no lubrication, no rust-related wear, and no need for chain tension adjustments—factors that currently drive visits to local bike shops. Instead, upkeep might focus more on electrical diagnostics and software updates, potentially altering the skill sets required at establishments like Mellow Johnny’s Bike Shop or Jack Brown’s Beer, Wine & Bicycles. Simultaneously, the ability to fine-tune ride characteristics via software opens doors for municipal fleet management—imagine Austin’s bike-sharing program adjusting pedal resistance profiles based on real-time congestion data or weather conditions, all pushed over-the-air.

Of course, this technology doesn’t exist in a vacuum. It builds on years of e-bike evolution, from the early torque-sensing models that popularized natural-feeling assist to the current wave of connected, app-integrated rides. What’s new here is the complete decoupling of human input from mechanical output, placing software as the indispensable interpreter. Patents like the one filed by HL Mando (referenced in KR20250008185A) show that even traditional automotive suppliers are exploring these control strategies, suggesting the concept has legs beyond boutique startups. For Austin, a city that positions itself as a tech-forward hub with a strong environmental ethos, this convergence could attract interest from entities like the Austin Chamber of Commerce’s clean tech initiatives or the University of Texas at Austin’s Cockrell School of Engineering, where research into human-vehicle interaction and sustainable mobility is already underway.

Given my background in urban systems analysis, if this trend toward software-defined, chainless e-bikes gains traction in Austin, here are the three types of local professionals residents should consider connecting with—not as specific endorsements, but as categories to evaluate based on verified criteria:

• Urban Mobility Planners with Micro-Mobility Expertise: Look for professionals affiliated with organizations like the Capital Area Metropolitan Planning Organization (CAMPO) or the City of Austin’s Active Transportation Program. Key criteria include demonstrated experience in integrating emerging micro-mobility technologies into long-term transportation plans, familiarity with Texas Department of Transportation (TxDOT) guidelines for bike infrastructure, and a track record of community engagement processes that incorporate resident feedback on new transit modes.
• Specialized E-Bike Technicians Focusing on Electrical Systems: Seek out shops or independent technicians who hold certifications from recognized bodies like the Light Electric Vehicle Association (LEVA) or have completed manufacturer-specific training on mid-drive and hub motor systems. Essential qualifications involve proficiency in diagnosing electrical faults (using multimeters and diagnostic software), experience with battery management systems, and a clear protocol for firmware updates on connected e-bike platforms—distinct from traditional mechanical tune-ups.
• Human Factors Engineers or Ergonomics Consultants: Prioritize individuals with academic backgrounds in biomechanics, kinesiology, or industrial design, ideally affiliated with research institutions like UT Austin or consulting firms serving transportation clients. What to verify: experience conducting user studies on cycling ergonomics, understanding of how power delivery curves affect perceived exertion, and the ability to assess whether software-defined ride characteristics genuinely reduce physical strain or cognitive load across diverse rider demographics.

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