A Look At The ‘Other’ Gig Workers

The term “gig worker” in India immediately paints a picture of platform workers such as ride-hailing drivers, food delivery agents, and home service providers. However, gig workers are actually a spectrum of workers spread across various sectors, across urban-rural economies too. According to the Ministry of Labour & Employment, Government of India, a gig worker is "a person who performs work or participates in a work arrangement and earns from such activities outside of traditional employer-employee relationship.” Thus, a gig worker could be anyone from a plumber who services through their own network or a beautician associated with an app or even freelance graphic designer.

India’s gig economy is projected to encompass 23.5 million workers by 2029-30, according to NITI Aayog estimates, up from 7.7 million in 2020. IT sector gig workers — such as those in app development, cybersecurity, web design, cloud services, and data analytics — form a rapidly growing subset, driven by the proliferation of technology platforms offering flexible, remote, and short-term assignments.

So, while there has been plenty of buzz around platform workers and their future of work, there is a section of urban gig workers that is often ignored in these mainstream conversations — the gig workers within the IT sector, i.e. the freelancers and contract professionals who deliver specialised digital and technical services. As Artificial Intelligence picks up pace, these gig workers are silently emerging as a pivotal force, reshaping the contours of opportunities and challenges within the IT sector.

A Tug of War between Opportunities and Challenges

While AI brings opportunities for efficiency and innovation, robust legal, social, and economic safeguards are needed to ensure sustainable livelihoods for India’s IT gig workforce. AI is fundamentally changing the operational landscape for gig workers in the IT sector. From automated job matching to algorithmic opacity, AI has brought in a complex mix of opportunities as well as challenges.

Opportunities:

• Automated Job Matching and Dynamic Pricing: AI-driven marketplaces quickly match freelancers to relevant jobs and clients, shortening hiring cycles and enabling real-time compensation adjustment based on skill sets, market demand, and task complexity.
• Remote and Hybrid Work Models: The growth of cloud-based platforms and AI-powered collaboration tools has made remote gig work routine and viable at scale, reducing absorptive barriers while expanding competition globally. This is especially instrumental in integrating India’s skilled labour force with the world market.
• Job Transformation & Upskilling: AI adoption has had a different impact at different levels. While it is displacing entry-level jobs, it has been transformational for mid-level roles in the sector. The demand for advanced skills in machine learning, cybersecurity, cloud infrastructure, and solution architecture has accelerated over the last couple of years. According to NASSCOM, nearly 70% of India’s current and future workforce feel AI/ML and cybersecurity will dominate job prospects, compelling gig workers to continuously upskill. The incentive to upskill a large workforce within a short span of time has never looked better for India.

Challenges:

• Job displacement: While mid-level jobs are getting transformed, giving workers an opportunity to upskill, the entry-level routine jobs in IT, such as basic coding and testing, are increasingly automated, leading to layoffs. The current skilling initiatives lack strategic implementation to bridge this gap in the labour force that can enable just transition at a rate to match the job displacement.
• Income Instability and Irregular Benefits: Gig contracts often lack social security, health benefits, or retirement cover. More importantly, the policy roadmap for gig workers largely focusses on platform workers. This is further exacerbated by limited access to collective bargaining, owing to the nature of the work which leaves little room for unionisation.
• Algorithmic Opacity: Decision-making processes on gig platforms are increasingly automated, making pay and work allocation less transparent and often subject to algorithmic bias. There are no specific policy interventions to support algorithmic transparency at the moment.

Paving Way for Multistakeholder Solutions

Much like most policy interventions in India, there is a tendency of stakeholders to operate in silos when it comes to worker welfare. For starters, the understanding of welfare or social security must go beyond just monetary benefits and include access to livelihood opportunities and skilling as well. Second, there are many unconventional solutions from around the globe that have preserved the ‘contractual’ status of gig workers, while ensuring they have access to significant benefits.

• Institutionalising skill development: The approach to skill development in the IT sector has been mostly based on the prerogative of the worker. Countries like Australia have institutionalised mandatory upskilling through government-led schemes that support displaced gig and IT workers moving to higher-paying jobs. Businesses are incentivised to conduct regular training sessions via tax breaks and subsidies.
   
  
  1. Singapore, meanwhile, runs a long-standing national upskilling framework (SkillsFuture) that subsidises training, provides lifelong learning accounts, encourages employer training partnerships, and uses labour-market analytics to target high-demand skills (AI, cybersecurity, cloud). This combines demand signals with training subsidies to accelerate redeployment of displaced workers.
  2. For India, a national regulatory commission can champion phased, government-funded reskilling and upskilling initiatives, with mandatory participation from businesses and incentives like tax credits for those investing in worker education.
     
     
• Building algorithmic transparency: The European Union is pioneering rules that require platforms/IT firms to disclose how algorithms determine task allocation, pay, and deactivation. More businesses now publish payment policies and justify worker deactivation decisions, reducing arbitrary actions. India needs transparent algorithmic management rules that grant clarity on pay/task allocation logic and enable workers to contest unfair algorithmic decisions.
• Centralised industry-specific Labour Market Information Systems (LMIS): A centralised LMIS for the IT sector can integrate job posting data and employer surveys into a skills observatory to target training before mass displacement. Such models have already been tested in Singapore and pilot initiatives can be run for key metro cities before being scaled up.

Looking Ahead 

India’s gig economy is a diverse, rapidly expanding landscape, now encompassing millions of workers across both blue-collar and specialised IT sectors. As AI accelerates digital transformation, IT sector gig workers are quietly becoming critical agents of change. The current policy framework remains overly focused on traditional platform workers, leaving significant gaps for IT-based gig professionals. Bridging these divides demands robust, multistakeholder solutions. Only with proactive and coordinated action can India ensure that its digital gig workforce achieves sustainable livelihoods, supports ongoing innovation, and continues powering the future of work in a rapidly changing economy.