- Grab rolls out 13 AI features for matching, routing and pricing.
- AI efficiency protects margins instead of passing on costs.
What happened
Grab, a Southeast Asian superapp offering ride-hailing, food delivery and financial services, is using AI and scale to counter rising fuel prices and softer demand, as geopolitical tensions push up energy costs and strain consumer spending.
Grab is leaning on artificial intelligence and platform scale to manage rising fuel costs, according to chief executive Anthony Tan. Speaking to Reuters, Tan said the company is focusing on affordability as external pressures weigh on both drivers and users.
Fuel prices have risen amid Middle East tensions, increasing operating costs for drivers. At the same time, consumers are becoming more cautious, slowing demand growth across ride-hailing and food delivery services.
Grab has introduced 13 new AI-driven features to improve efficiency across its platform. These include tools for better driver-passenger matching, route optimisation and dynamic pricing. One example is its “group ride” feature, which uses algorithms to split fares among passengers and can cut costs by up to 40%, according to the report.
Tan said the company’s strategy is to use technology to reduce prices rather than pass on higher costs. Grab’s large user base and data scale allow it to continuously train AI systems, improving efficiency and lowering costs over time.
Why it’s important
AI-led efficiency is reshaping platform economics, allowing firms to offset inflation while driving demand through lower prices and better user experience.
Grab’s approach reflects a structural shift in the platform economy. Growth is no longer driven by subsidies alone. Instead, companies are using AI to optimise operations and pricing in real time. This helps maintain demand without eroding margins.
Scale is becoming a critical advantage. Larger platforms can generate more data, enabling better AI models and stronger cost control. This creates a self-reinforcing cycle that smaller competitors may struggle to match.
The strategy also shows how tech firms are adapting to a tougher macro environment. Rising fuel costs and cautious spending are forcing companies to prioritise efficiency. AI is emerging as a core tool for balancing cost pressures with growth ambitions.
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