• Recently, following intervention by Union Labour Minister Mansukh Mandaviya, delivery platforms Blinkit, Zepto, Zomato, and Swiggy have decided to remove the 10-minute delivery system from their applications.

Reasons for the Change

• The shift comes in the aftermath of a nationwide gig-worker strike on New Year’s Eve 2025, during which delivery partners protested unsafe working conditions, heightened road accident risks, and psychological stress arising from rigid ultra-fast delivery targets.
• Subsequently, the Union Labour Ministry directed major quick-commerce and food-delivery platforms, including Blinkit, Zepto, Swiggy, and Zomato to discontinue time-bound delivery branding that could incentivise unsafe work practices.

About the 10-Minute Delivery Model

• The 10-minute delivery model is a quick-commerce (q-commerce) strategy that relies on a dense network of nearby dark stores, AI-driven demand forecasting, and algorithm-optimised logistics. It aims to provide extreme convenience by delivering groceries, essential goods, and select retail products within ten minutes of order placement, primarily in high-density urban areas.

Rise of the Gig Economy in India

• India’s gig economy—encompassing platforms such as Blinkit, Swiggy, Zomato, Zepto, Ola, and Uber—has expanded rapidly due to increased digital penetration, urbanisation, and a large young workforce. The sector has enabled flexible employment opportunities and economic inclusion, particularly for urban youth.

Trends of 10-minute delivery:

• Rapid expansion since 2021 with quick-commerce platforms competing on speed as a differentiator.
• Increasing use of algorithmic management to push delivery partners to meet tight timelines.
• Peak-demand dependence during festivals and late-night hours, intensifying work pressure.
• Rising worker mobilisation and strikes globally against hyper-speed delivery promises.

The Case for Banning 10-Minute Deliveries

• Road Safety & Public Risk: Ultra-compressed delivery timelines convert public roads into performance arenas, incentivising riders to violate traffic norms to avoid algorithmic penalties and income loss.
• E.g.  In Bengaluru delivery clusters, traffic police reports show spikes in wrong-way driving and signal jumping during peak “instant delivery” hours, directly linking speed targets to unsafe behaviour.

• Occupational Health Crisis: Algorithmic gamification pushes riders into prolonged high-stress cycles, where earnings depend on continuous hyper-alertness, leading to physical exhaustion and psychological burnout.
• E.g.  Medical clinics around Delhi-NCR dark stores report increased cases of back injuries, wrist strain, and anxiety disorders among riders working 10–12 hour speed-based shifts.

• Human Rights & Labor Dignity: Reducing workers to time-optimised “delivery nodes” strips them of rest, autonomy, and humane working conditions, undermining the principle of dignified labour.
• E.g.  Rider protests near quick-commerce warehouses highlight the absence of toilets, shade, or rest areas, revealing systemic neglect of basic workplace dignity.

• Externalization of Costs: Platforms internalize profits from speed while offloading fuel costs, vehicle depreciation, and accident risks entirely onto workers, distorting fair compensation.
• E.g.  Despite higher delivery intensity, riders report declining per-order earnings as bonuses replace stable pay, while repair and fuel expenses rise sharply.

• Regulatory Misalignment: The instant delivery model circumvents the employer’s duty of care by treating safety risks as individual choices rather than structural obligations.
• E.g.  This directly conflicts with the Code on Social Security, which mandates health protection and accident safeguards for platform-based workers.

However, several structural challenges persist:

• Uncertain incomes: Heavy dependence on incentives and low base pay.
• Algorithmic control: Task allocation, performance evaluation, and remuneration determined by opaque platform algorithms.
• High-pressure delivery models: Ultra-fast delivery targets heighten accident risks and compromise worker safety.
• Lack of social protection: Limited access to health insurance, accident coverage, and pension benefits.
• Strikes during peak demand periods, including Christmas 2025, highlighted these systemic issues, with tens of thousands of gig workers protesting unsafe conditions and precarious livelihoods.

Challenges in Regulating Instant Delivery:

• Consumer Dependency: Once hyper-convenience becomes habitual, political resistance grows against any regulation perceived as reducing consumer comfort.
• E.g.  During recent gig-worker strikes, public backlash against service suspensions revealed how instant delivery has become a perceived necessity rather than a luxury.

• Algorithmic Opacity: Opaque algorithms mask penalties through ranking and visibility controls, making regulatory detection and enforcement extremely difficult.
• E.g.  Instead of explicit fines, platforms silently deprioritize slower riders via “shadow-banning,” reducing orders without leaving auditable evidence.

• Policy Arbitrage: Inconsistent state-level regulation allows platforms to concentrate operations in regions with weaker labour protections.
• E.g.  States like Rajasthan with specific gig-worker laws contrast sharply with states lacking any framework, enabling regulatory evasion.

• Revenue vs. Safety Trade-off: Speed restrictions may reduce order volumes, creating fear among workers that safety reforms will cut their already precarious incomes.
• E.g.  Many riders hesitate to support bans as surge incentives linked to fast deliveries form a significant portion of daily earnings.

• Evasive Business Modeling: Platforms adapt language without changing pressure, maintaining unsafe expectations under new branding.
• E.g.  Rebranding “10-minute delivery” as “Fastest” or “Priority” preserves the same speed incentives while bypassing explicit bans.

Legal Recognition under the Code on Social Security, 2020

• The Code on Social Security (SS), 2020 marks a significant milestone by formally recognising gig workers and platform workers within India’s labour framework for the first time. Earlier, these workers were largely excluded from traditional labour laws such as the Payment of Wages Act, 1936 and the Employees’ State Insurance Act, 1948.

Key definitions under the Code include:

1. Aggregator: A digital intermediary connecting buyers with service providers.
2. Gig worker: An individual working outside a conventional employer–employee relationship for remuneration.
3. Platform worker: A person providing services through an online platform.
4. Platform work: Work arrangements facilitated digitally in exchange for payment.

Social Security, Welfare, and Portability

• The Code mandates the creation of a Social Security Fund, financed through contributions from aggregators such as Amazon, Flipkart, Swiggy, and Zomato. The Fund is intended to support health insurance, accident coverage, maternity benefits, and old-age pensions for gig and platform workers.
• It also introduces portability of benefits through the e-Shram portal, enabling workers to retain entitlements while switching platforms or occupations. A centralised database facilitates targeted welfare delivery, skill development initiatives, and grievance redressal mechanisms, including toll-free helplines and facilitation centres.

Way Forward

• Mandatory Safety Windows: Regulation should replace arbitrary time promises with distance- and traffic-calibrated delivery windows prioritising legal compliance.
• E.g.  A 5-km/20-minute cap aligns delivery expectations with urban traffic realities, reducing incentives for rule violations.

• Algorithmic Accountability: Platforms must disclose speed, pay, and penalty logic to ensure fairness and prevent hidden coercion.
• E.g.  Mandating Explainable AI audits would allow regulators to detect discriminatory or unsafe incentive structures.

• Inflation-Indexed Earnings: Stable livelihoods require pay structures that automatically adjust to rising fuel and maintenance costs.
• E.g.  Linking per-kilometre rates to CPI or fuel indices protects riders from real-income erosion.

• Judicial Oversight: Dedicated grievance forums are needed to address arbitrary de-platforming and wage disputes swiftly.
• E.g.  Karnataka’s proposed Grievance Redressal Officer model offers a template for speedy, worker-centric justice.

• Universal Social Security: Safety nets must be automatic and universal rather than optional or privately negotiated.
• E.g.  Shifting from opt-in insurance to state-mandated welfare boards ensures coverage irrespective of platform policies.

• The Social Security Code represents a paradigm shift by moving the gig workforce from an informal and vulnerable status towards legal recognition and protection. However, effective outcomes will depend on robust enforcement, regulation of unsafe delivery models, and greater transparency and accountability in platform algorithms. Ensuring fair wages, occupational safety, and comprehensive social security is crucial for building a resilient, inclusive, and formalised gig economy.

Conclusion

• Decision of Blinkit, Zepto, Zomato, and Swiggy to drop the “10-minute delivery” model underscore the need to balance consumer convenience with worker safety and dignity. It signals a broader shift within India’s quick-commerce sector towards responsible labour practices and stronger regulatory oversight, an essential step for sustaining a future ready digital economy.