Rising labor costs and shrinking delivery margins are prompting large platform operators like Grab to turn to automation. The acquisition of Infermove brings robotic capabilities in-house.
Grab operates at a scale where small efficiency gains can have a huge impact. The company’s platform supports millions of deliveries in Southeast Asia, many of which are made by passengers on scooters and bicycles in dense urban areas, creating complexities that limit the extent to which automation can replace human labor. By acquiring companies focused on robots designed for unstructured environments, Grab sees AI in the physical world becoming mature enough to be used in cases beyond pilot programs.
Delivery automation close to core operations
Grab has chosen to internalize its development loop rather than relying on off-the-shelf systems. Infermove’s technology is designed to learn from real-world movement data, including information generated by non-motorized delivery vehicles. In practical terms, this means the robots were trained on how people would actually move around sidewalks, intersections, and busy drop-off points, rather than how those spaces would appear on a simulation.
For delivery operators like Grab, that distinction is important. While simulated environments can support early development, they often struggle with the edge cases that define real cities. By bringing that learning process in-house, Grab can shape how automation works under its own operational constraints, rather than adapting its delivery network to third-party systems.
From a company’s perspective, the strategic value lies in control. Owning this technology gives Grab greater leverage over deployment pace, operational scope, and cost trade-offs. It also reduces long-term dependence on vendors whose priorities may not align with Grab’s regional reach or economic realities.
However, automation is not positioned to replace human riders. While robots may take over some of the workflow, humans remain at the center of service delivery. Grab’s interest appears to be focused on selective usage, such as structured first-mile or last-mile segments where tasks are repetitive and distances are short. In these sectors, robots could help smooth out spikes in demand, reduce delays during peak periods, and ease the pressure of labor shortages.
Address cost pressures without service interruption
At an internal meeting in December, Grab’s chief technology officer Suesen Thomas called Infermove’s progress “impressive,” highlighting both the technology and its early commercial use. He also said the company will continue to operate independently and its founders will report directly to him. This structure suggests that Grab prioritizes execution and continuity over rapid organizational integration.
This approach reflects a broader shift between large digital platforms. Rather than treating AI as an added layer on top of existing systems, companies are embedding it deep into their core operations. In the shipping and logistics sector, this often means moving beyond optimization software to physical automation, with higher risks and costs, but with more structural potential benefits.
The timing is also telling. On-demand delivery volumes continue to grow, but margins remain under pressure. Customers expect faster service and lower rates, but operators face rising wages, fuel costs and tighter regulations. In such an environment, automation is less about novelty and more about maintaining service levels without compromising profitability.
Bringing robot development closer to operations could also help align incentives around data use. Training physical AI systems requires large amounts of real-world data, which is already being generated at scale by delivery platforms. Keeping feedback loops internal speeds up iteration and reduces the need to share sensitive operational data externally.
There are still limits. Robots designed for sidewalks and short routes are unlikely to replace human couriers across networks in the near future. Weather, local rules, and customer acceptance will continue to shape where automation realistically works. Deploying across multiple countries adds further complexity, as infrastructure and regulations vary widely.
Industry forecasts suggest rapid growth for last-mile delivery robots, but these numbers provide limited guidance for operators. The more pressing question is whether automation can reduce cost per delivery without introducing new points of failure. It depends on performance in a real environment rather than market size.
From a company perspective, the Infermove acquisition is not a bet on robots as a product category. This is a move to strengthen the collaboration between AI, data, and physical operations. For logistics and mobility-based platform companies, that integration can be a key element in managing growth under sustained cost pressures.
(Photo provided by Afif Ramdasma)
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