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Scaling Autonomous Freight: Inside Pony.ai’s Robotruck Business

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From Gen-4 heavy-duty trucks to L4 urban delivery vehicles, shared technology, automotive-grade production and industry partnerships are shaping the next phase of Pony.ai’s autonomous freight business

GUANGZHOU, CHINA, Aug. 6, 2026 /PRNewswire/ — At a media briefing on August 3, He Xing, Vice President of Pony.ai and Head of the company’s Robotruck business, discussed how nearly a decade of technology development in L4 autonomous driving is beginning to support broader commercial deployment in freight transportation.

Over the next two to three years, Pony.ai expects 500 to 1,000 Gen-4 autonomous heavy-duty trucks to be deployed across three primary scenarios in China: long-haul freight, bulk commodity transportation and port logistics. Pony.ai also expects light-duty trucks to scale faster, with a longer-term goal of reaching 100,000 L4 autonomous light-duty trucks by 2030.

The targets reflect several developments coming together: a more mature autonomous driving system, lower hardware costs, automotive-grade redundant vehicle platforms and deeper collaboration with vehicle manufacturers and logistics operators.

Why freight, and why now

Pony.ai began developing autonomous trucks in 2018. The first vehicles were largely hand-built prototypes. Subsequent generations moved progressively closer to automotive-grade production through partnerships with truck manufacturers. The question gradually shifted from whether the technology could work to where and under what conditions, it could create the most operational value.

Road freight presents a clear need. The industry faces persistent structural pressures, including a shortage of qualified heavy-duty truck drivers, an aging workforce and sharp fluctuations in demand during peak seasons. Long hours, overnight driving and demanding routes can also increase fatigue-related safety risks. L4 autonomy can help supplement freight capacity, particularly on repetitive routes and during overnight or peak-demand periods that are difficult to staff, while supporting safer, more consistent operations.

Turning that potential into scaled operations, however, requires more than technical capability. Autonomous driving systems must also be safe, reliable, ready for automotive-grade production and commercially viable at scale. For Pony.ai, the timing of large-scale deployment has therefore also depended on bringing down the cost of the autonomous driving system.

“We had been waiting for the right moment,” He said. “Our truck technology had already reached a high level, but the cost of building an L4 Robotruck remained high. The reduction in ADK costs benefited both heavy- and light-duty trucks. That is why we did not rush into large-scale production earlier.”

Pony.ai’s Gen-4 autonomous heavy-duty truck has reduced autonomous driving hardware costs by approximately 70% compared with the previous generation. Developed for automotive-grade mass production, the vehicle is designed for a service life of 20,000 operating hours or up to 1 million kilometers.

The Robotruck business has already moved beyond technology testing. As of November 2025, Pony.ai operated a fleet of around 200 trucks and had transported more than 1 billion ton-kilometers of freight. In the first quarter of 2026, Robotruck services generated US$10.2 million in revenue, up 31% from a year earlier, driven primarily by the expansion of commercial operations.

Two vehicle platforms across the freight network

Pony.ai’s current Robotruck strategy covers both heavy-duty trucks and light-duty trucks, reflecting the different roles they play across the freight network.

Heavy-duty trucks are designed primarily for transportation between logistics hubs, including long-haul highway freight, bulk commodity routes and port transportation. Pony.ai’s Gen-4 models are based on battery-electric platforms and support both single-vehicle autonomous operation and L4 platooning, depending on the requirements of each operating environment.

Production of the Gen-4 heavy-duty trucks is now underway. Vehicles are expected to roll off the production line in batches and enter commercial service across several use cases over the coming months.

Shenzhen’s Mawan Port will be among the first deployment sites. Pony.ai has secured a project there and expects to deploy dozens of Gen-4 Robotrucks for commercial operations. Ports represent one of the three priority scenarios for the platform, alongside highway freight and bulk commodity transportation in western China.

Light-duty trucks address a different part of the logistics chain. They are commonly used between urban distribution centers, retail stores, delivery outlets and cold-chain facilities—environments that overlap substantially with the complex urban road conditions in which Pony.ai’s Robotaxis already operate.

Pony.ai introduced its first L4 autonomous light-duty truck in April 2026. Co-developed with CATL and built on CATL’s Kunshi Chassis Platform, the vehicle uses automotive-grade components and a fully redundant safety architecture. It offers approximately 18 cubic meters of cargo space and is intended for both urban and intercity freight.

The first vehicles have now entered intensive road testing in operating environments provided by logistics partners. Initial use cases include express delivery, retail distribution and food and beverage cold-chain logistics. Pony.ai plans to pursue the regulatory approvals required for fully driverless operation as testing and validation progress.

Based on current operating assumptions, Pony.ai estimates that fully driverless light-duty trucks could reduce per-kilometer operating costs by 40% to 50% compared with conventional human-driven operations. The vehicle can also carry 2.6 times the cargo volume of mainstream low-speed autonomous delivery vehicles, while operating at speeds suitable for regular urban and intercity roads.

One Virtual Driver across vehicle types

Pony.ai’s approach is built around applying the same underlying Virtual Driver technology across Robotaxis, heavy-duty trucks and light-duty trucks.

The light-duty truck uses the same core technology stack as Pony.ai’s Gen-7 Robotaxi. Because the two platforms operate in many of the same urban environments, they can also share supporting infrastructure and operating capabilities, including charging, ground support, service centers, fleet management and remote assistance. Pony.ai estimates that the overall technological and operational synergies between the two platforms exceed 90%.

Heavy-duty trucks require more vehicle-specific adaptation. Their size, weight, mechanical structure and longer braking distances create different control requirements, while highway and bulk commodity routes introduce operating conditions not commonly encountered by passenger vehicles. Even so, the core capabilities used to understand traffic, interact with other road users and make driving decisions draw on the same underlying technology and development methodology.

Data and operating experience from the different vehicle platforms also contribute to a shared development loop. PonyWorld 2.0, Pony.ai’s proprietary world model, is designed to identify areas where the Virtual Driver requires further improvement, guide targeted data collection and support more efficient training and evaluation.

“Autonomous driving has to progress step by step—from technology driving product development, to the product enabling a business model, and ultimately to that model reshaping the industry,” He said.

The shared safety architecture is equally important. Pony.ai’s current Robotaxi, heavy-duty truck and light-duty truck platforms use redundant systems covering steering, braking, communication, power supply, computing and sensing. This fail-operational design allows a vehicle to maintain core driving functions and select an appropriate location to pull over safely if certain hardware or software components fail.

A partner-led route to scale

Scaling autonomous freight requires more than producing vehicles. It also requires access to freight demand, established operating networks, maintenance capabilities and infrastructure such as logistics hubs and charging facilities.

Pony.ai has therefore built its Robotruck business around partnerships with vehicle manufacturers and logistics operators. Its Gen-4 heavy-duty trucks were developed in collaboration with manufacturers including SANY Truck, while the light-duty truck was co-developed with CATL. Pony.ai also works with Sinotrans across long-haul freight and urban logistics scenarios.

The commercial model can vary depending on the maturity and requirements of a project. In some earlier-stage deployments, Pony.ai participates more directly in vehicle ownership and freight operations through a Transportation-as-a-Service, or TaaS, model. This allows the company and its partners to validate operating performance and unit economics in real commercial environments.

As the market matures, Pony.ai expects partner-led deployment under an Autonomous Driving-as-a-Service, or ADaaS, model to play a larger role. Under this model, vehicle manufacturers produce the trucks, logistics partners own and operate the fleets, and Pony.ai provides its Virtual Driver and related technical services. Some port projects are already beginning to adopt this approach.

“We are not here to run e-commerce or postal services ourselves,” He said. “Our role is to become a partner to logistics companies and integrate into the systems they already use to serve their customers.”

This structure allows each participant to focus on its established strengths: vehicle manufacturers on automotive-grade production and sales, logistics companies on freight demand and fleet operations, and Pony.ai on autonomous driving technology.

The next phase

The next phase of Pony.ai’s Robotruck business will focus on ramping up production of the Gen-4 heavy-duty truck and deploying it in commercial projects, while completing the testing and regulatory work required to deploy the light-duty truck at scale.

In heavy-duty trucking, Pony.ai will initially focus on highway freight corridors, bulk commodity routes and ports. The company is also exploring an innovative model for highway transportation, which could simplify trailer handoffs between autonomous highway operations and human-driven first- and last-mile delivery.

For light-duty trucks, the immediate focus is to work with logistics partners to validate operations in express delivery, retail distribution and cold-chain transportation. The ability to operate overnight when drivers are more difficult to recruit and fatigue-related safety risks are higher could become an early commercial use case.

Pony.ai also sees potential for autonomous trucks in overseas markets, particularly at ports and other well-defined logistics sites where driver shortages and labor costs strengthen the economics of automation.

For Pony.ai, the objective is not simply to place more autonomous trucks on the road. It is to build a repeatable operating model in which technology, vehicles, infrastructure and freight demand can scale together.

View original content:https://www.prnewswire.com/news-releases/scaling-autonomous-freight-inside-ponyais-robotruck-business-302844854.html

SOURCE Pony AI Inc.

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Sagility India Enters Asia’s Top 60 Best Workplaces, Ranked 57 in 2026

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With over 22,000 employees in India and over 49,000 globally, Sagility continues to expand its workforce Employee initiatives span learning, leadership development, wellbeing and engagement, including STRIDE, PACE, STEP, S.H.E. Leads Program. S.H.E. Circle & Abilities Circle ERG, Wonder of Wellness and Healthcare Academy.  

MUMBAI, India, Sept. 21, 2026 /PRNewswire/ — Sagility India has been ranked 57 among Asia’s Best Workplaces 2026 in the Large Companies category by Great Place to Work. The company was ranked 11 among India’s Best Companies to Work For 2026, following previous Great Place to Work recognitions. 

The Asia ranking is based on confidential employee feedback, with more than 3.8 million individual responses representing nearly 8.9 million employees across the region. Employees evaluated their workplace experiences across areas including trust, innovation, company values, and leadership. Of the 100 organizations recognized in the Large Companies category, 34 operate in India. 

The findings also point to the importance of everyday employee experience. Overall sentiment among employees at the Best Workplaces in Asia remained high at 93%, while the Trust Index Grand Mean stood at 91%. More than 95% of employees reported positive sentiment on areas such as feeling safe, being welcomed, being treated fairly, and feeling proud to belong. 

Against this backdrop, Sagility’s workforce has grown to over 22,000 employees in India and more than 49,000 globally. Its people initiatives span career development, continuous learning, wellbeing, and employee engagement. Key programmes include the STRIDE, PACE, and STEP leadership development programmes, the S.H.E. Leads Program for women in junior and mid-level roles, S.H.E. Circle and Abilities Circle Employee Resource Groups, Wonder of Wellness initiatives, employee surveys, hobby clubs, and Healthcare Academy. Sagility also operates a hybrid work model.

Speaking about the recognition, Tina Vas, Chief Human Resources Officer, Sagility, said, “At our scale, culture is about what employees experience every day. Can they learn, speak up, get support, and see an opportunity to grow? Those are the things we pay attention to. We are thrilled to be recognised as the 57th Best Workplace in Asia, building on our earlier recognition as the 11th Best Workplace in India. These recognitions reinforce our commitment to our employees and their well-being, particularly because they are grounded in what employees themselves say about their experience.”

The latest recognition reinforces Sagility’s focus on building a workplace where employees have opportunities to learn, develop and contribute, while fostering an environment that supports wellbeing, inclusion and engagement. As the organization continues to evolve, these priorities remain an important part of its people strategy. 

About Sagility Limited

Sagility is a tech-led, U.S. healthcare-focused business operations solutions and services company that supports payers, providers, and their partners in delivering best-in-class operations, enhancing member and provider experiences, and improving the quality of care, all while ensuring cost-effective financial and clinical outcomes. With over two decades of experience, Sagility’s dedicated experts address complex healthcare challenges through deep domain expertise and technology innovations. The company serves six of the top ten payers in the U.S., utilizing its advanced technology, processes, and solutions to ensure efficient operations and minimize additional administrative costs. The company delivers these services through its skilled talent pool of nearly 50,000 professionals across five global service delivery centers located in the US, India, the Philippines, Jamaica, and Colombia.

To learn more, visit: https://sagility.com/ 

Media contact details:

Srushti Rao | press@sagility.com

View original content:https://www.prnewswire.com/in/news-releases/sagility-india-enters-asias-top-60-best-workplaces-ranked-57-in-2026-302884023.html

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New Research from Kai: UK CISOs Face a Widening AI Security Gap as Attackers Gain the Advantage

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59% of UK CISOs say attackers already hold the advantage, while 67% take more than a week to remediate critical vulnerabilities

SAN JOSE, Calif., Sept. 21, 2026 /PRNewswire/ — Kai, the company behind the first agentic AI cybersecurity platform designed to execute security work end-to-end at machine speed with human expert accuracy, today announced UK findings from its inaugural 2026 State of Autonomous Defense Report.

According to the survey of 100 UK CISOs, a growing gap is emerging between the speed of AI-powered attacks and the ability of security teams to respond. As AI makes it faster and easier for attackers to find and exploit vulnerabilities, many UK organisations are still relying on manual processes that can take days or weeks. The findings point to a looming challenge for defenders: security operations built around human speed may not be able to keep pace as attackers become faster and more automated. If organisations fail to close vulnerabilities quickly, attackers could strike before defenders have a chance to act, increasing the risk of a major security incident.

While UK CISOs recognise the need to move toward machine-led security, many organisations aren’t there yet. Barriers including trust, governance and operational readiness could slow that transition as attackers continue to accelerate their use of AI.

“AI is changing the speed of cyberattacks, and security teams can’t afford to fall further behind,” said Nick Degnan, Chief Revenue Officer at Kai. “The concern is that attackers are getting faster while many defenders are still operating with processes built for a different era. UK organisations know they need to change, but moving from human-led to machine-led security takes trust, governance and a willingness to let machines take on more of the work. The longer that transition takes, the more room attackers have to pull ahead.”

UK CISOs understand the AI threat, but many security processes remain human-led

UK security leaders overwhelmingly acknowledge that AI has changed the threat landscape. Nearly all UK CISOs (94%) say their organisation is prepared to defend against AI-accelerated vulnerability exploitation, yet only one-third (33%) describe themselves as very prepared.

That confidence comes as UK CISOs see attackers gaining the upper hand. Fifty-nine percent believe attackers currently have the advantage given current levels of AI adoption and advancement, compared with just 13% who believe defenders have the advantage. As attackers increasingly leverage AI to accelerate exploitation, many UK organisations continue to rely on human-led security workflows that struggle to keep pace.

Slow remediation leaves a growing window for attackers

The research found that vulnerability management remains heavily dependent on manual effort, leaving UK organisations exposed and security teams under pressure. More than half (54%) of UK organisations report their vulnerability and exposure management processes are at least half manual, while 67% require more than one week to remediate critical vulnerabilities. More than half (54%) say at least one-quarter of known vulnerabilities go unremediated for more than 30 days.

These operational challenges are taking a measurable toll on UK security teams. Eighty-four percent of UK CISOs say vulnerability and exposure management contributes at least moderately to security team burnout, including 19% who describe it as a major contributor.

The findings suggest it’s not just the threat landscape creating risk, but the operating model itself.

UK organisations want more automation, but barriers remain

While UK organisations increasingly see automation as essential to keeping pace with AI-powered threats, confidence in autonomous decision-making has yet to catch up. More than half (51%) of UK CISOs identify lack of trust in automated decisions as one of the biggest barriers to broader automation adoption in vulnerability and exposure management, followed by governance or compliance concerns (45%) and skills or talent gaps (45%).

UK organisations are already embracing automation for lower-risk activities such as vulnerability prioritisation (57%) and asset discovery and inventory (55%). However, only 32% currently permit automated remediation actions without human approval, underscoring that most organisations remain cautious about letting machines change the environment.

UK CISOs are also clear about what would give them greater confidence in machine-led security. More than half point to vendor accountability and liability protections (54%), auditability and explainability (53%), and regulatory clarity (52%) as factors that would increase their confidence in allowing machine-led systems to execute remediation actions without human approval.

UK organisations are moving toward machine-led security, but attackers are moving faster

Despite today’s challenges, the research shows UK organisations are further ahead in adopting machine-led approaches. Today, 46% of UK organisations describe their vulnerability and exposure management approach as mostly or primarily machine-led, compared with 35% of organisations globally.

The foundations for further adoption are also being put in place. Ninety-four percent of UK CISOs say their organisation’s governance approach is either already designed to support machine-led security actions or is being adapted for greater machine-led operation.

Looking ahead 12 to 18 months, 40% expect humans to supervise machine-led systems that lead prioritisation and execution, while 25% expect most vulnerability and exposure management workflows to be machine-led and 14% expect autonomous security operations to become the primary operating model.

The findings suggest UK organisations are not waiting for machine-led security to become a future reality. Many are already putting it into practice, while adapting governance and operating models for a more autonomous approach to cyber defence. But as attackers gain speed through AI, the pressure to make that transition is only growing.

Read the full 2026 UK State of Autonomous Defense Report here.

Methodology

The Kai Survey was conducted by Wakefield Research among 500 CISOs at private sector companies with a minimum annual revenue of $500 million, including 100 CISOs in the United Kingdom. The research was conducted in four markets between June 15 and June 29, 2026, using an email invitation and an online survey. All UK findings cited in this release are based on the 100 UK respondents.

About Kai

Kai is the AI company rebuilding cybersecurity for the machine-speed era. Trusted by Fortune 500 and Global 2000 enterprises, the Kai Autonomous Defense Platform replaces fragmented tools and human-limited workflows with agentic AI that works continuously across cyber asset management, application security, infrastructure vulnerability management, and detection engineering. It contextualises, reasons, and acts at machine speed and enterprise scale. What takes human-led teams weeks, Kai executes in hours, driving risk toward zero through Auto Remediation. Human defenders don’t just keep up. They become superhuman.

Media contact: kai@inkhouse.com 

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Bloomsbury Money Group appoints ex-SAP engineer as CTO to head global banking platform

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LONDON and ST HELIER, Jersey, Sept. 21, 2026 /PRNewswire/ — Bloomsbury Money Group today announced the appointment of Thomas Holst as Chief Technology Officer. Holst joins from SAP’s health technology ecosystem, and brings more than a decade of experience building data-intensive, privacy-critical technology platforms. As CTO, he will lead Bloomsbury Money’s technology strategy, engineering and platform architecture as the Group builds out its banking platform across its existing markets and the new jurisdictions it plans to enter.

The appointment comes as Bloomsbury Money moves from a regulated money services and virtual asset business run from Jersey and London to a multi-jurisdiction financial network. The group’s current services span multi-currency accounts, foreign exchange, cross-border payments and regulated virtual asset custody and transfers. Under Holst, these are being brought together on a single platform designed from the outset to run in several regulatory environments with one standard of control.

A single network for cross-border value

Bloomsbury Money’s ambition is a global cross-border payment network in which fiat currencies, regulated digital assets and local payment rails sit side by side. In practice that means a customer can hold, convert and send value in the form that suits the transaction, whether a wire, a card payment, a domestic instant payment or a digital asset transfer, and the network selects the fastest compliant route. The group intends to extend its regulatory footprint and rail connectivity market by market to deliver this, starting in Jersey and the Channel Islands.

Manu Choudhary, co-founder, Bloomsbury Money Group, said:

“Thomas is joining at the right moment. We have a regulated business, live customers and a clear view of where cross-border money movement is heading. What we need now is someone who can turn that view into infrastructure that behaves the same way in every market we enter. Thomas has spent thirteen years building software where getting the data wrong is not an option, and that is the mindset we want at the centre of this platform. Our ambition is a network where a business in one country pays a supplier in another in whatever form of money and over whatever rail makes sense, without ever thinking about the plumbing.”

Chris Park, CEO, Bloomsbury Money Group, said:

“I have spent over decades in banking and the pattern never changes: the firms that last are the ones whose technology is boring in the right ways. Resilient, auditable, fast, and built by people who assume a regulator will one day ask to see how every decision was made. Thomas comes from SAP and from healthcare technology, one of the few sectors where the bar on data protection and control is as high as it is in finance. He has shipped enterprise software to hospitals and research institutions and built and run the cloud infrastructure underneath it. Bringing that discipline to a platform spanning fiat, digital assets and local rails across several jurisdictions is exactly the point. This is an infrastructure hire, and infrastructure is what we are building.”

Thomas Holst, Chief Technology Officer, Bloomsbury Money Group, said:

“Most fintechs bolt new products onto an old core. Bloomsbury Money is building the core with the network in mind from day one: multiple currencies, multiple asset types, multiple rails and multiple regulators, all held to the same standard of control. That is a rare engineering brief and it is why I said yes. I spent more than a decade in SAP’s health technology ecosystem in Germany, building products for hospitals and researchers and health data applications and the cloud infrastructure they run on. Both taught me that trust is a technical property. It comes from architecture, testing and auditability, not from a marketing deck. I will bring the same standard here.”

About Bloomsbury Money Group

Bloomsbury Money Group provides multi-currency accounts, spot foreign exchange, cross-border payments, Visa debit cards and regulated virtual asset services to businesses and individuals, with offices in St Helier, Jersey and London. Its regulated business, Bloomsbury Money Jersey Limited, is a money service business and virtual asset service provider regulated by the Jersey Financial Services Commission (JFSC registry reference 210054). Bloomsbury Money Group Limited is a Jersey private company (registered number 165853). The group is building a global cross-border network for fiat, digital assets and local payment rails, and trades under the strapline Better Global Banking.

www.bloomsburymoney.com

Forward-looking statements

This announcement contains some forward-looking statements about Bloomsbury Money Group’s strategy, plans and intended products, including the development of its platform, the extension of its services into additional jurisdictions. These statements reflect current intentions and expectations and are subject to regulatory approvals, technical development, market conditions and other factors outside the group’s control. They are not a guarantee of future performance or of the availability of any product or service in any jurisdiction, and the group undertakes no obligation to update them.

Media contacts: Bloomsbury Money Press Office
Email: press@bloomsburymoney.com
Website: www.bloomsburymoney.com

View original content:https://www.prnewswire.com/news-releases/bloomsbury-money-group-appoints-ex-sap-engineer-as-cto-to-head-global-banking-platform-302882570.html

SOURCE Bloomsbury Money

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