The cost of ‘free’ in India

The word ‘free’ carries a unique emotional and political charge in India. It signals relief, generosity, access, and sometimes even justice. In a country marked by deep inequality and historical deprivation, the idea of receiving something without having to pay for it feels not just attractive but morally right. Free school meals, free healthcare camps, free ration, free mobile data, free apps, free advice—these are not fringe phenomena but central features of everyday life. Yet as ‘free’ becomes more pervasive, it becomes more urgent to interrogate what it actually costs in reality. Because nothing in this world is truly free. Even when money is not exchanged, value is still transferred, quietly, unevenly, and often invisibly.

The digital revolution has made ‘free’ feel natural, even inevitable. India’s smartphone explosion, driven by affordable devices and some of the world’s cheapest mobile data, has brought hundreds of millions online in a short span of time. For first-time internet users, free apps are often the internet itself. Messaging platforms, video-sharing apps, digital wallets, navigation tools, shopping platforms, and learning apps promise unlimited access at zero cost. Downloading them requires no financial transaction, only a tap on a screen. This apparent absence of cost masks a different economy altogether, one where data, attention, and behaviour are the currencies being traded.

Every free app extracts value as it collects personal information, tracks usage patterns, studies preferences, and monitors behaviour across platforms. In return for convenience and access, we surrender fragments of our digital selves, often without fully understanding the implications. In India, where digital literacy has not kept pace with digital adoption, this exchange is especially asymmetrical. We routinely accept terms and conditions that we cannot realistically read or comprehend, granting permissions that would be alarming if framed in simpler language. Location data, contact lists, browsing habits, voice samples, and even biometric identifiers become assets in a vast data economy. We do not pay in rupees, but we pay in terms of our privacy, autonomy, and long-term exposure.

This is not a small concern, as data is power, and not merely information. When aggregated at scale, it allows companies to predict behaviour, shape consumption, influence opinion, and nudge decision-making. In India, where hundreds of millions engage daily with free digital platforms, this concentration of behavioural data in private hands has far-reaching consequences. It affects what we see, what we buy, how we think, and even how we vote. The cost of free apps is not just about individual privacy but collective vulnerability to influence and manipulation. What appears to be a harmless trade in terms of free services for data becomes a structural imbalance when we lack meaningful choice or awareness.

Free apps are designed to maximise engagement because engagement drives advertising revenue. Endless scrolling, autoplay videos, push notifications, algorithmic recommendations, and gamified feedback loops are not accidental features; instead, they are engineered mechanisms to capture and hold attention. Time spent on these platforms is monetised elsewhere, converted into impressions, clicks, and behavioural insights. For us, this translates into hours lost daily to digital consumption. The opportunity cost is immense in terms of time not spent on learning, work, rest, relationships, or reflection. In a country where time poverty is already acute for large sections of the population, the extraction of attention is a high but rarely acknowledged cost of ‘free.’

Alongside free apps, free government schemes occupy a central place in India’s public imagination. Welfare programs offering free food, free electricity, free healthcare, free education, and direct cash transfers are often framed as moral imperatives in a society with widespread poverty. And indeed, many such schemes have had transformative impacts. Free school meals have improved nutrition and attendance. Subsidised healthcare has saved lives. Social security schemes have provided safety nets in times of crisis. To dismiss free schemes outright would be both inaccurate and unjust.

However, the scale and politics of ‘free’ in governance demand scrutiny. Government schemes are funded by public money, either through taxation or borrowing. When services are offered for free, the cost is distributed across society, including future generations. Fiscal resources are finite, and every rupee allocated to a subsidy is a rupee not spent elsewhere. The real question is not whether the state should provide support, but how that support is designed, targeted, and sustained. Poorly designed free schemes can strain public finances, crowd out long-term investments, and create distortions that are difficult to reverse.

One of the most persistent risks associated with free government schemes is the shift from empowerment to dependency. When benefits are delivered without clear pathways to capability-building, translating into skills, livelihoods, ownership, or agency, they can trap beneficiaries in cycles of reliance. This is not a failure of intent but of design. Welfare that does not evolve into opportunity risks becoming permanent relief rather than temporary support. Over time, political incentives can encourage the expansion of free entitlements without corresponding investments in productivity, institutional capacity, or economic growth. The cost, then, is borne in slower development, rising debt, and reduced fiscal flexibility.

There is also a less visible social cost when citizens begin to relate to the state primarily as a provider of free goods rather than as a facilitator of opportunity, and expectations shift. Accountability becomes transactional, and long-term policy thinking gives way to short-term appeasement. This dynamic can erode democratic deliberation, reducing complex governance challenges to simplistic promises of free distribution. In such an environment, the language of rights is often mixed with the politics of giveaways, weakening the deeper idea of citizenship rooted in participation, contribution, and shared responsibility.

In India, ‘free’ advice is abundant and rarely priced. Friends, relatives, colleagues, social media influencers, and anonymous online forums dispense guidance on everything from investments and careers to health, parenting, and mental well-being. At one level, this reflects strong social bonds and collective problem-solving. Knowledge-sharing has always been part of Indian society. But in the contemporary context, the proliferation of free advice, especially online, has begun to undermine the value of expertise itself. Professional knowledge is produced through years of education, training, practice, and ethical accountability. When expert advice is expected to be free, its perceived value diminishes. Professionals are pressured to give away labour without compensation, while advice-seekers are encouraged to treat complex problems as easily solvable through quick opinions. The result is often superficial guidance applied to situations that demand nuance. In fields like finance, law, and health, the consequences can be serious, resulting in misdiagnoses, financial losses, legal complications, and long-term harm.

Digital platforms have amplified this problem dramatically. Social media rewards confidence, not competence. Algorithms favour content that is engaging, simplified, and emotionally charged. As a result, the loudest voices often drown out the most qualified ones. Free advice becomes entertainment, stripped of context and accountability. Influencers monetise indirectly through advertising, brand deals, or lead generation, while audiences consume advice under the illusion that it is altruistic. The hidden cost here is the ability to distinguish reliable knowledge from persuasive noise.

Behavioural economics shows that people disproportionately favour free options, even when they are inferior to low-cost alternatives. The absence of price triggers a sense of gain that overrides rational evaluation. In India, this bias plays out repeatedly when users choose free digital services with weak privacy protections over modestly priced, safer alternatives,  beneficiaries prefer immediate free benefits over long-term investments in capability, or individuals trust free advice over paid expertise because payment itself is mistaken for bias. These patterns are not signs of ignorance but of how human psychology interacts with scarcity and aspiration.

Free social media platforms, while enabling connection, intensify comparison. Carefully curated images of success, beauty, and happiness circulate endlessly, shaping aspirations and insecurities. The cost is stress, anxiety, and diminished self-worth, especially among young users. These effects are not accidental side-effects but structural outcomes of platforms designed to maximise engagement rather than well-being.

When platforms subsidise services to gain scale, smaller players struggle to compete. Local businesses, creators, and service providers are often forced into ecosystems where they generate value but capture little of it. Revenue flows upward and outward, concentrating power in a few large entities. Price signals weaken, making it difficult for sustainable, high-quality alternatives to emerge. Over time, consumers accustomed to free access become resistant to paying for quality, undermining the viability of independent work and innovation.

Yet it would be a mistake to conclude that free is inherently harmful. Free education, free public healthcare, free libraries, and free public infrastructure have historically been among the most powerful tools for social progress. The issue is not free versus paid, but opaque free versus conscious free. When free services are transparent about costs, respectful of users, and oriented toward empowerment rather than extraction, they create genuine public value. When free becomes a strategy to harvest data, attention, votes, or dependency, its costs far outweigh its benefits.

The challenge for India is to develop a more mature relationship with ‘free.’ This requires stronger regulation of digital platforms, particularly around data protection, transparency, and competition. It requires better design and evaluation of welfare schemes, ensuring they build capabilities and not just deliver consumption. It requires cultural shifts that restore respect for expertise and recognise that paying for knowledge is not exploitation but investment. And most importantly, it requires citizens to ask harder questions when something is offered at no cost.

Who is paying for this? What am I giving up? Who benefits in the long run? Is this making me more capable or more dependent? These questions are not cynical, but are of utmost importance. In a complex society, the absence of price does not mean the absence of cost. It only means the cost has been displaced onto privacy, time, dignity, judgment, or the future. India’s relationship with ‘free’ will shape its developmental trajectory in profound ways. If used wisely, then free access can level the playing field and unlock human potential; else it can deepen inequalities, hollow out institutions, and quietly extract value from those least equipped to see it. Free is never just an economic choice; instead, it is a moral, political, and social one. And in a country as large and consequential as India, the true cost of free is something we can no longer afford to ignore.

Why good projects struggle for funding

The social impact sector’s irony is that some of the most thoughtful, community-centred, transformative projects struggle to secure funding, while others that are not so well designed, and sometimes even superficial, find their way into donor portfolios. This contradiction is often explained as a failure of proposal writing or organisational capacity, but such explanations only scratch the surface. The deeper truth lies in understanding donor behaviour, including the incentives, constraints, and biases that shape funding decisions. Good projects are overlooked not because they lack merit, as ‘merit’ is not the primary currency in the funding ecosystem, but because of factors like alignment, risk perception, measurability, and institutional incentives.

At the core of the problem is the simple fact that donors do not fund the ‘best’ projects; instead, they support those that align with their priorities. Every donor operates within a specific thematic, geographic, and strategic framework, often influenced by board directives, political factors, or institutional legacy. A project that is highly relevant to a particular community may still be rejected if it does not fit neatly into a donor’s current focus areas. This creates a subtle but significant distortion in the sector, as organisations begin to design projects around donors’ language and preferences rather than the lived realities of communities. In this process, genuinely valuable ideas can become invisible, not because they lack worth, but because they are misaligned with funding narratives.

This is further compounded by the deeply risk-averse nature of development funding. Donors are not neutral actors, and they are accountable upward to their boards, governments, shareholders, or trustees. This shapes a cautious approach to funding, where the emphasis is on minimising risk rather than maximising impact. Established nonprofits with proven track records are preferred over emerging grassroots organisations, even when the latter may have deeper contextual understanding. Similarly, tried-and-tested models are favoured over experimental or innovative approaches. The consequence is a filtering mechanism that systematically excludes many high-potential projects simply because they appear uncertain or difficult to manage. Ironically, the very qualities like innovation, localisation, and adaptability that make a project transformative are often the ones that make it seem risky.

Now there’s a growing emphasis on measurability in funding decisions. Donors desire clear metrics, defined outputs, and quantifiable results for results-based management and data-driven accountability of projects. While this has enhanced transparency, it has also created a bias toward interventions that can demonstrate immediate, tangible results. Projects focused on infrastructure, service delivery, or training programmes tend to perform better because their outputs are easily measurable. Conversely, initiatives aimed at changing social norms, empowering communities, or strengthening institutions struggle to articulate their impact within the same frameworks. The most complex and deeply rooted development challenges are often the least measurable within the funding cycle, and therefore the least fundable. Good projects operating in these areas are disadvantaged not because they are ineffective, but because their effectiveness cannot be readily quantified.

The nature of donor engagement further complicates the picture, despite frequent references to ‘partnership,’ much of development funding remains transactional. Organisations submit proposals in competitive, opaque processes with limited opportunity for dialogue or feedback. In such an environment, relationships matter enormously. Organisations with prior visibility, networks, or access to donor ecosystems often have a significant advantage, even if their projects are not fundamentally stronger. Trust, built over time, can outweigh the intrinsic quality of a proposal. Conversely, new or lesser-known organisations, particularly those operating at the grassroots level, find it difficult to break into these networks. As a result, good projects often fail not on their own terms, but because they are evaluated in isolation, without the benefit of relational context.

This dynamic is closely tied to a broader structural bias within the global development ecosystem. Local organisations, despite being closest to the communities they serve, receive only a small fraction of direct funding. Donors frequently cite concerns around compliance, financial risk, and administrative capacity, which leads them to channel funds through larger intermediaries. While this may simplify management from the donor’s perspective, it creates a distance between resources and realities. Local initiatives, which may be highly effective and deeply embedded, often remain underfunded or entirely excluded. This is not merely an operational issue, but reflects an implicit hierarchy of trust, where proximity to power and familiarity with donor systems are valued over contextual knowledge and lived experience.

Equally important is what might be called the ‘proposal illusion’, with the tendency to compare the quality of a project with the quality of its documentation. In practice, donors assess proposals, not projects. This places a premium on articulation, structure, and the ability to translate complex realities into donor-friendly language. Organisations with access to skilled writers, consultants, or international exposure are better positioned to succeed, even if their fieldwork is not exceptional. On the other hand, grassroots organisations that may be doing outstanding work often struggle to present it in ways that resonate with donor expectations. The result is a system where storytelling can overshadow reality, and where good projects are overlooked because they are not packaged effectively.

Time horizons further skew funding decisions as donors tend to operate within short funding cycles, typically ranging from one to three years, with success evaluated within this limited timeframe. This creates a preference for projects that can demonstrate quick wins, rather than those that require sustained engagement over longer periods. Yet most of the development challenges, like education reform, livelihood transformation, and social cohesion, are inherently long-term and demand patience, continuity, and iterative learning. When funding is short-term, even well-designed projects can struggle to show meaningful results, making them less attractive to donors. This leads to what is often described as the ‘pilot trap,’ where innovative ideas receive initial funding but fail to scale or sustain due to a lack of long-term commitment.

Another big challenge is the persistent reluctance to fund organisational overheads. Donors often prefer to allocate resources directly to programmatic activities, placing limits on administrative costs such as salaries, systems, and governance. This undermines the very foundations that enable effective implementation. Strong organisations require robust systems, skilled personnel, and institutional stability. When these are underfunded, the quality of implementation suffers, reinforcing donor perceptions of risk and inefficiency. This creates a vicious cycle in which organisations are unable to build capacity, and good projects become difficult to execute at scale.

Underlying all of these factors are the incentives that shape donor behaviour. Funding decisions are rarely neutral as they are often influenced by a range of external and internal considerations. Corporate donors are often guided by brand alignment and visibility, favouring projects that can be showcased or communicated easily. Philanthropic foundations may be influenced by leadership vision, legacy goals, or thematic interests. In each case, the logic of funding extends beyond impact alone. Good projects that do not align with these broader incentives may struggle to gain traction, regardless of their potential.

Bilateral and multilateral donors operate within geopolitical frameworks, where aid allocation may reflect strategic interests as much as development priorities. In the wake of global economic slowdowns, traditional sources of Official Development Assistance (ODA) are shrinking. The U.S., U.K., and several European governments have all announced significant cuts to their ODA budgets. These reductions should have sparked debates about the failures of the aid system, but they largely passed with little reflection. The outcome is a development finance environment that’s simultaneously more selective and more risk-averse. Funders now prioritise large-scale, measurable, and politically ‘safe’ projects that can boast short-term, quantifiable results. Small-scale social initiatives, particularly those addressing systemic or cultural issues like inequality or governance, find themselves outside the funding radar. Even when progressive funding streams exist, for example, climate justice or inclusive innovation programs, they come wrapped in new conditionalities of alignment with national development strategies, ESG benchmarks, or private-sector co-financing. These conditions further alienate grassroots actors who can’t meet such formal requirements.

It is also important to acknowledge a more fundamental constraint of scarcity, as the pool of available funding is limited, while the number of worthy projects is vast. Even in a perfectly functioning system, not all good ideas can be supported. This introduces an element of competition that is not purely based on merit. Projects must not only be good, but must also be timely, visible, and strategically positioned. In such an environment, marginal differences in presentation, alignment, or relationships can determine outcomes, leaving many strong proposals unfunded.

Projects that are technically sound but insufficiently rooted in community realities often struggle to convince donors of their sustainability. Funders have been increasingly looking for evidence of participation, co-creation, and local ownership. However, these elements are difficult to demonstrate within conventional proposal formats, leading to a gap between genuine engagement and its representation. Good projects that are deeply participatory may still fall short if they cannot adequately convey this dimension to donors.

These dynamics suggest that the funding ecosystem does not necessarily reward the intrinsic quality of projects. Instead, it rewards alignment, clarity, measurability, and perceived reliability. This does not mean that donors are acting in bad faith; rather, they are responding to their own constraints and accountability structures. The system, in many ways, is functioning as designed. However, the consequences are significant, as innovative, context-specific, and potentially transformative projects often remain unfunded, while safer, more conventional interventions dominate.If we are serious about tackling poverty, inequality, and climate injustice, we must start by rethinking how funding itself operates. It is not enough to design good projects, but one must also learn to translate them into the language of donors without diluting their essence. This requires strategic proposal architecture, effective communication, and relationship-building. For donors, the challenge is more profound as it involves rethinking risk, expanding definitions of impact, and creating funding mechanisms that are flexible, inclusive, and long-term. Without such shifts, the sector will continue to produce good ideas that never see the light of day, not because they are unworthy, but because they do not fit the system that is meant to support them.

Code Dependent: Living in the Shadow of AI

Code Dependent: Living in the Shadow of AI

by Madhumita Murgia | 320 Pages | Genre: Non-Fiction | Publisher: Pan Macmillan | Year: 2024 | My Rating: 5/10

My life—and yours—is being converted into such a data package that is then sold on. Ultimately, we are the products.”
― Madhumita Murgia, Code Dependent

Code Dependent is a collection of case studies about people who are affected by technology, without the rigour and analysis that I was expecting. But then it is not an academic or research-oriented book, but more in the popular non-fiction genre. Several of the case studies in the book reflected on the dark side of technology and social media manipulation of individuals and communities, and their rights, privacy, freedom and future.

The book is an account of how the algorithms used by tech in our daily lives through the user-friendly apps like Google Maps, Uber, Instagram, Facebook and others are changing us and the way we see the world. Our data and us as data is continuously being used for targeted advertisements to make businesses grow fatter.

Murgia defines AI as “a complex statistical software applied to finding patterns in large sets of real-world data.” I believe that AI is much more than Statistical Pattern Recognition (SPR), and this viewpoint of the author is quite narrow.

I agree with Murgia’s take on emergence of new data colonialism around the worlds, especially in under-developed and poor economies, where sub-contracting create numerous jobs as data workers, but wealth created in not shared equitably. ‘Informed Consent’ seemed misinterpreted in the book, and subjective.

There was less of ‘AI’ and more of ominous ‘shadows’ in the book. While the book talks about algorithmic bias against people, it certain has flavours of human bias against technology from the author. The book read more on data transparency than demystifying the positives and negatives of AI and technology. Pessimistic views due to advancement in technology is more pronounced throughout the book.

The book is still a fascinating read, with glimpses of ‘how AI is altering the very experience of being human’.

Data is Divine

In God we trust. All others must bring data.” This quote, made by W. Edwards Deming holds true (and may even supersede God for some as Divine).

I have been in love with data right from my school years and the mysteries of the world it holds. I have tried to develop data driven models on human relationships, the movement of animals, finding patterns in the ways of the world, and later designing programs of social impact for challenging poverty, and policy development. In the end, we all are data, from the moment we are an idea until long after we pass away.

“Data is divine” highlights the growing understanding of data’s vital significance in modern society, in much the same way that religious or spiritual values have directed civilizations throughout history. In today’s digital age, data powers innovation, decision-making, and advancement in all fields, including governance, research, business, healthcare, and lifestyle.

1. Data as a source of truth: Data is frequently regarded as an impartial depiction of reality, providing information on trends and occurrences that may be imperceptible to anecdotal experience or intuition. In this way, data has a unique position as the basis for making well-informed decisions and uncovering hidden facts.

2. The power of data in innovation: Data is driving advancements in domains like healthcare, finance, and climate science and is revolutionizing industries as it powers AI/ML and sophisticated analytics. This emphasizes how data has the “divine” ability to spark significant change. The use of data for enhancing human welfare, from preventing pandemics through data-driven epidemiology to lowering inequality by studying societal trends has been in use. When applied sensibly and morally, it can aid in resolving some of the most pressing issues facing society.

3. Data as omnipresent: From the apps we use daily to the systems that manage our cities, data is present everywhere in the modern world. Its pervasiveness is comparable to a certain “divine” quality in that it affects almost every facet of contemporary life, whether we are conscious of it or not.

4. Data and ethics: Data carries a great deal of responsibility along with its power. Similar to supernatural knowledge, there are significant ethical ramifications to the way we collect, use, and safeguard data. Data misuse can result in inequality, manipulation, and privacy violations. As a result, it is crucial to handle data with dignity, openness, and ethics.

“Data is divine” also implies that we must treat it with deference and accountability while simultaneously appreciating its immense importance in shaping our future. We need to balance the power of data with ethical considerations as our world grows more and more data driven. The following are some crucial strategies to preserve this equilibrium,

1. Data privacy and informed consent: People ought to be in charge of how their information is gathered, kept, and utilized. It is not appropriate to force them to divulge information. Companies must be open and honest about their data practices so that users know what information is being gathered and why. Clear and informed consent should not be buried in complicated terms and conditions. Data literacy is essential among general population so that they are aware of the consequences of disclosing personal information, and the dangers of data misuse.

2. Data minimization: Only gather information that is absolutely required for the current job. This reduces the possibility of abuse and shields people from needless exposure. I’ve seen in recent years how social development initiatives gather and store vast amounts of data, with donors coercing their nonprofit partners to obtain it, yet this doesn’t address any societal issues. It is crucial to have a conscious grasp of what is needed.

3. Data bias and fairness: AI/ML systems may reinforce or increase biases found in the training data. Therefore, diversifying datasets, employing inclusive development techniques, and reviewing algorithms for bias are all necessary to ensure fairness.

4. Equitable data access: One way to lessen inequality is to make sure that data access and its advantages are shared equitably among all communities. This entails preventing the reinforcement of systemic disadvantages while ensuring that marginalized groups have access to data-driven insights.

5. Data governance and accountability: To ensure that data is utilized properly, organizations and governments must establish robust data governance policies and ethical frameworks. To stay up with the latest developments in technology, these policies must be revised regularly. It is imperative to establish unambiguous lines of accountability for the handling and utilization of data. Data practices can be kept moral and in line with social standards with the support of independent oversight organizations or ethics boards.

6. Regulation and legal safeguards: Strong data protection laws that impose restrictions on how businesses and organizations can gather, keep, and handle personal data must be enforced by governments. Laws that address issues like accountability for algorithmic judgments, eliminating discrimination, and safeguarding human rights in AI-driven systems are crucial for the ethical application of automation and artificial intelligence. Because technology is changing so quickly, regulatory models must be adaptable and flexible to support innovation and enable quick responses to emerging ethical dilemmas.

7. Data for social good: Data can and should also be used positive social impact including lowering inequality and poverty, combating climate change, and improving public health. Governments, corporations, and civil society organizations working together can help guarantee that data is used morally and for the good of society. These collaborations may result in common frameworks for the ethical use of data.

A multifaceted strategy including legislation, transparency, public education, and proactive governance is needed to strike a balance between the power of data and ethical issues. Prioritizing the defence of individual rights, maintaining equity, and advancing the common good while fostering innovation should be the main goals of ethical data use. Through cultivating a culture of accountability and responsibility, we can leverage data’s promise (and divinity) without sacrificing moral principles.

Disclaimer: The opinions expressed are those of the author and do not purport to reflect the views or opinions of any organization, foundation, CSR, non-profit or others

Cover Photo: This is an AI generated image.