Introduction — Mobility as a social technology

We are at a rare inflection point where advances in perception, prediction and planning meet an urgent social need: populations are aging, cities are growing, and daily commutes are consuming time that people can never get back. Autonomous vehicles are often discussed as a technology novelty or a logistics play. I want to frame them differently: as infrastructure for dignity, productivity and inclusion.

This is not a distant, sci‑fi promise. Today’s robotaxi systems, scaled responsibly, can restore mobility to millions who face shrinking travel options — the elderly, caregivers, shift workers and those trapped in long, unproductive commutes. Our technical roadmap and financing choices are shaped by that human imperative.

Demographics, commutes and the human cost

Across Asia, Europe and North America, demographic charts show a simple, relentless trend: more older adults, often concentrated in suburbs and peripheral neighborhoods that are poorly served by public transit. At the same time, urban sprawl and economic geography mean longer travel times for work and care. The cost is not just economic: it’s social isolation, lost working hours, stress and diminished health outcomes.

Conventional transit investments are essential but slow and capital‑intensive. The deployable, incremental approach offered by robotaxis can complement mass transit — filling gaps, operating off‑peak when buses are rare, and providing door‑to‑door service for people who can’t or shouldn’t rely on private cars.

How robotaxis help — practical, measurable impacts

• Independence for older adults: Scheduled rides with intuitive user interfaces, voice assistance, and in‑vehicle remote support let older passengers maintain routines without the physical or cognitive burden of driving.
• Reduced commute overhead: When travel time becomes productive time instead of stressful driving time, cities capture economic value and residents gain hours back each week.
• Last‑mile and off‑peak coverage: Dynamic routing allows fleets to concentrate service where demand is thin for buses but meaningful for citizens — late nights, weekends, and suburbs.
• Lower emissions and congestion: Shared robotaxis can reduce vehicle miles traveled per passenger and complement electrification to deliver cleaner mobility.

AI realities: what it takes to deliver safe, scalable robotaxi service

Building an operational robotaxi system is an AI engineering challenge at scale. In research it is elegant to talk about single‑model breakthroughs. In the city, success depends on reliability, redundancy and continuous learning. The stack we’re investing in emphasizes:

1. Robust perception: sensor fusion across cameras, lidar and radar; models that generalize across weather, lighting and city morphologies; and systems that quantify uncertainty in real time.
2. Behavioral prediction: probabilistic forecasting of pedestrians, cyclists and other vehicles — not as single trajectories, but as distributions of possible futures that a planner can hedge against.
3. Safe planning and control: motion planners that incorporate comfort metrics, regulatory constraints, and formal safety envelopes; control stacks that degrade gracefully and hand back to humans when needed.
4. Simulation and transfer: extensive sim‑to‑real pipelines with domain adaptation so models trained in virtual cities transfer safely to asphalt and glass towers.
5. Fleet intelligence and operations: real‑time routing, prediction of demand, remote supervision, and mechanisms to update models with new edge cases discovered in the field.

These are not separate problems but a single, integrated engineering discipline: agent design at city scale. The architecture choices we make have immediate social consequences — from accessibility features in the UI to the way a vehicle approaches an intersection where an elderly pedestrian is crossing.

Capital and alignment: the role of Chinese investors

Capital is not neutral. Where money comes from shapes speed, focus and partnership. We have been fortunate to attract long‑term Chinese investors who bring patient capital, industrial partnerships and deep connections across manufacturing, mapping and mobility ecosystems.

That type of backing is powerful because it enables a two‑pronged strategy: accelerate product development and ensure manufacturability at scale. Partnerships with local vehicle makers and suppliers reduce the friction between prototype and production, which is essential if urban operators are to trust and adopt robotaxi fleets.

Importantly, these investors understand the regulatory environment and urban challenges in China, which is often the crucible for high‑frequency, high‑density robotaxi operations. Lessons learned there translate to other markets with appropriate localization.

Why Hong Kong for R&D and public listing

We have chosen Hong Kong as the primary venue for a public raise to fund a new phase of R&D. There are several reasons this makes strategic sense for a company building the next generation of mobility:

• Access to international capital: Hong Kong connects Asian capital with global investors who understand technology company growth cycles.
• Talent magnet and gateway: Hong Kong is a hub for engineering talent, universities and cross‑border collaboration, which helps us recruit and retain top AI and systems engineers.
• Regulatory and commercial agility: The city’s role as a financial center provides mechanisms for governance, transparency and the cross‑border deployment of services.

Most critically, funding raised through a public offering will be ring‑fenced to accelerate core R&D: perception and compute stacks, large‑scale simulation infrastructure, and the safety certification processes needed to scale deployments ethically.

How R&D investments translate into societal impact

Here is how we will direct capital to create measurable outcomes within a three‑ to five‑year horizon:

• Scalable perception hardware: invest in sensor stacks that balance cost and capability so robotaxis become affordable to operate across many neighborhoods.
• Edge compute innovations: lower latency and energy per inference so vehicles can make safer decisions while extending range and reducing operating cost.
• Simulation and data platforms: build the virtual cities and continuous learning loops to shorten the time between encountering a novel scenario and updating every vehicle in the fleet.
• Human‑centered product design: interfaces for older adults, multi‑lingual voice systems, and remote assistance functions that make rides reliable and dignified.

Each dollar spent on R&D must be judged by two metrics: improvements in safety and improvements in accessibility. If the science does not move people — especially those losing mobility options the fastest — then we have missed the point.

Regulation, trust and the path to acceptance

Technology alone does not create adoption. Public trust is earned through transparency, demonstrable safety and collaborative policymaking. We engage with city regulators, transit agencies and community groups early — not after a service is built — to co‑design how robotaxis integrate into urban mobility networks.

Transparent reporting of incidents, independent audits of safety systems and clear data governance practices are part of our public commitment. For populations that are vulnerable, such as older adults, that trust is the difference between adoption and rejection.

The AI research community’s role — collaboration at scale

Progress in autonomy will not be a solitary achievement. It requires a vibrant ecosystem: open benchmarks for perception and prediction, shared simulation environments, and reproducible safety evaluations. We are committed to collaborating where shared infrastructure accelerates safety and accessibility across the industry.

There is room for commercial differentiation and public good in the same landscape: shared tools to validate models and private innovations that deliver unique operational advantages.

What success looks like

In five years I want to see measurable changes in how people live and move: fewer hours lost to commuting, increased independence among older citizens, a demonstrable modal shift from private cars to shared autonomous fleets, and a clear safety record that improves continuously through data and engineering.

For our company, success is not only measured in deployed vehicles but in lives improved: an elderly parent who can visit friends without planning a whole day around transit, a nurse who can take shorter, more reliable rides between shifts, a city that breathes easier because fewer single‑occupancy car trips thread its arteries.

Closing — a pragmatic optimism

I am optimistic, but not naive. The road to scaled, safe robotaxi service is long and requires patient capital, relentless engineering and deep civic partnership. That is why we are focusing R&D investment on the problem areas that matter most: perception under rare conditions, simulation fidelity, edge efficiency and human‑centered interfaces.

Our decision to pursue funding via a Hong Kong offering is about more than capital; it is about building an integrated research and deployment base that connects regional strengths with global talent and governance. With aligned investors, rigorous AI engineering and a commitment to social outcomes, robotaxis can move from a technological curiosity to a platform that enhances dignity and productivity across cities.

To the AI community reading this: this is a call to build systems that scale not only in performance benchmarks but in human impact. Let us design autonomy that serves the breadth of urban life — from the longest commute to the most fragile passenger — and do so with openness, humility and rigor.