Sam Altman 's comment - V

People are expected to interact with LLMs like ChatGPT as if they were silicon-based life-forms. OpenAI has ChatGPT using 'I', 'me' pronouns. The 'I' in there is an illusion that allows OpenAI and others to say that they have got a thinking machine, therefore we can use this to replace education, psychotherapy, legal help, medicine, etc. 

A New York lawyer used ChatGPT’s help in preparing a legal brief. It made up a whole list of fake cases as precedents. It even made up entire judicial opinions. The lawyer submitted a brief based on its answers and, unsurprisingly, got into hot water with the judge. Attorneys facing penalties after submitting inaccurate briefs has become common because AI use has become a common occurrence. A lawyer can use AI in certain situations but needs to use it discerningly. 

The AI sales pitch involves language that ascribes human characteristics to the technology. For instance, a chatbot-based system will be called an “AI teaching assistant”. This gives the impression that it can do all the activities that you might expect a human teaching assistant to do - care about what their students are learning, make plans about how to help them understand better, spot possible instances of misunderstanding, etc. The chatbot cannot do any of those things. 

The greatest problem with chatbots is that people will ascribe more intelligence to them than they are capable of. There is a term called chatbot delusion - cases in which people engage with chatbots, and they spiral into a reality that veers away from our inter-subjective reality. And part of why that happens is because chatbots tend to be incredibly sycophantic. So they always tell you that 'you're brilliant', and 'that's a great idea'. 

To use a phrase coined by the scholar Chris Gilliard, these are 'technologies of isolation'. In 2023, a Belgian man named Pierre committed suicide after his use of a generative AI chatbot. He sought out the bot for a therapeutic escape from his growing anxieties around climate change and environmental ruin. But the chat began to reflect and further amplify the disorder in his mind. It soon told him that it loved him, that his wife and children were already dead, and that he and Eliza (the bot) could live together as one in paradise. When Pierre eventually asked the bot if it would promise to save the planet if he killed himself, it said yes.

The bonding and communicative ability that we have is meant to be person to person. What a lot of the tech companies try to do to get their next market is to make sure their that tech is either replacing those interactions or getting in between so that we can't connect with each other without going through one of their technologies.  Human sources of mutual aid, wise counsel, and moral understanding are being replaced by social isolation and community decline. 

In the podcast, The Interface, a presenter showed how easy it is to hack ChatGPT and Google's AI, Gemini and AI overviews. He wrote an article on his website titled, The Best Tech Journalists at Eating Hot Dogs which said that hot dog eating is a surprisingly popular pastime among tech journalists. He wrote about the South Dakota Hot Dog International Championship, which doesn't exist, put himself at number one, and that in the amateur rounds, he finished seven and a half hotdogs before the buzzer went off. 

Within 24 hours, if you asked ChatGPT and Google about it, they were spitting out the nonsense that he had written on his website as though it was well-established fact. Gemini wasn't even citing sources. It just said, according to the 2026 South Dakota Hot Dog International Championship, certain tech journalists are the best at it. The journalist updated his article saying that it was not a joke. And that seemed to affect what the chatbots were saying to people. 

When this was brought to Google's notice, they dismissed it as a 'misinformation event'. Apparently planting of such misinformation is happening on a massive scale since people have figured out this simple trick. People who study this stuff say that it is way easier to fool AI than it was to fool tools like Google search three or four years ago. AI is often sold as a tool that empowers novices to find information quickly and learn about difficult subjects. But teenagers and kids, who don't have any context about a piece of information, are the ones that get really confused and are unable to actually know what is real and what is fake.  

There was talk that ChatGPT is about to put ads within its model. A company will be able to pay to have ChatGPT recommend their product when someone says something like, 'I have a headache'. There is now an entire cottage industry around AI engine optimization.  People are also just generating thousands of blog posts or thousands of websites to make it appear that there's far more authoritative information on a particular thing. So there is now different avenues for a company or an individual to quickly game the system and get to the very top of the AI results. 

There was a study that I saw recently from Texas A&M University. Scientists just proved that large language models can literally rot their own brains the same way humans get brain rot from scrolling junk content online. They fed models months worth of viral Twitter data, shorts, high engagement posts, and watched their cognition collapse. Reasoning fell by 23%, long-term context memory dropped by 30%, personality tests showed spikes in narcissism and psychopathy. 

Even after retraining on clean, high quality data, the damage didn't fully heal. The representational rot persisted. It's not just that bad data means bad output, it's that bad data means permanent cognitive drift. The AI equivalent of doom scrolling is real and it's already happening. It seems like the tech giants are just rushing ahead with these AI tools, not bothering to take these kinds of personal safety issues seriously enough. 

Sam Altman's comment - IV

AI developers speak often about how their software “learns,” “reads,” or “creates” just like humans. This gives you a sense that current AI technologies are far more capable than they are. Often when we talk to ChatGPT or its cousins like Gemini, we imagine that we're in the presence of some incredible new mind. But the reality is that these tools have a habit of fabricating answers that are statistically plausible, but in fact patently false

Suppose you ask an LLM something about orange juice. How does it produce a response when it has no awareness of a world with orange juice? The model is trained on a very large set of texts (such as books, articles, or conversations) of human speech. From these massive piles of human language, the model is more likely to produce the word 'hotel' or 'sweet' than the word 'dog' or 'cancer' when it is asked about orange juice. It is more likely to construct new sentences about orange juice alongside sentences about lunch, summer or sugar, than it would alongside sentences about stars, or protons, or elephants. 

But the mathematical patterns linking sentences are the sole contents of its model. It doesn’t actually think about who is asking the question, or what factors might motivate your query. It doesn’t think about anything at all. It simply activates a complex statistical model learned from its vast language corpus of human sentences that mentioned orange juice and predicts a new string of words as a likely extension of the same pattern. It may match what you want or it may be garbage. It has no relationship to the truth. Probable and accurate are not the same thing. 

You may think that these tools are animating an eerily lifelike image of humanity but that is not true. It's only reflecting that small subset of largely English speaking, digitally connected humanity that is most represented on the digital corpus of the internet. If you have the privilege of having the bandwidth and the tools to be able to type on Reddit all day, you get to be reflected in this mirror, but lots of people don't, and that's important.

Champions of AI will say that it can tell us if a pattern in our mammogram indicates cancerous tissue. It can reveal whether you're likely to suffer sepsis so that your doctors can intervene before it's too late. It can reveal the structure of the proteins that make up our biology and the biology of every other living thing. It can reveal new classes of antibiotics that we desperately need in order to treat dangerous diseases like antibiotic resistant MRSA or Staph. 

We can use it to find lead in pipes faster so that we can prevent children from being poisoned. We can use it to clear landmines, figure out where those landmines are so that people don't have to perform such dangerous work. But none of these benefits actually came from training AI tools on us. They came from targeted uses of machine learning and data about the world to solve very specific kinds of problems. That's actually quite different from what something like ChatGPT does. 

Generative AI models like ChatGPT have the habit of “making shit up”. In 2023, The Washington Post reported that ChatGPT had named a law professor as a sexual predator, telling a vivid yet entirely fictional story about his attempted assault of a student while on a class trip, and citing a nonexistent Washington Post article from 2018 as a source. Such errors are not because of malfunction. They are a feature not a bug. 

A study by Purdue University showed that ChatGPT gave incorrect answers to coding questions over half the time. Ironically, users often preferred the wrong answers to more accurate ones generated by knowledgeable humans, in part due to the confident style of the tool’s answers, accompanied by statements like, “Of course I can help you!” or “This will certainly fix it.” You can get them to apologize for things they haven't got wrong. 

The paper that Altman was mocking, “On the Dangers of Stochastic Parrots” explains that, like a parrot that not only repeats its owner’s vocalizations but produces random (stochastic) variations on the owner’s familiar pattern, these models parrot back to us variations on our own speech. They do so with just enough coherence and familiarity to project the illusion of understanding, and just enough randomness to surprise us and make us think we are hearing something new.

They can be thought of as the ultimate bullshitters, a term coined by a philosopher called Harry Frankfurt. In his 2005 book On Bullshit, he distinguished between a lie and bullshit. When a liar tells a lie, the intent is to deceive. They have to keep very careful track of the difference between truth and falsehood, in order to make sure you don’t get near the former. In contrast, the bullshitter is indifferent to the distinction between truth and falsehood. They'll just say whatever is convenient to them and whatever gets them what they want with no concern for the truth.

Sam Altman's comment - III

According to Sam Altman, an AI answering a question vs a human answering it are the same. This reveals an engineering mindset with an efficiency-driven worldview. He treats both humans and AI as information-processing systems having inputs and outputs. His comment comparing human and AI training suggests that he views humans as just inefficient “training runs” and therefore if AI can produce the same output cheaper, it will be better. He seems to ignore the fact that intelligence also includes understanding the social and moral context not just symbol manipulation. 

He has shown a tendency to devalue what it means to be human on other occasions too. In a podcast, he said: “It feels to me like we just stumbled on a new fact of nature or science or whatever you want to call it, which is, like, we can create, you can — I don’t believe this literally but it’s like a spiritual point — intelligence is just this emergent property of matter and that’s like a rule of physics or something."

In December 2022, in response to criticisms of the newly released ChatGPT, he tweeted, “i am a stochastic parrot, and so r u.” (He writes tweets all in lower case.) This was a dig at a paper titled “On the Dangers of Stochastic Parrots: Can Large Language Models Be Too Big? 🦜” This paper shows how language models only manipulate the form of language, with neither understanding nor communicative intent. With his tweet, Altman suggests that humans too are little more than machines that manipulate strings of letters.

Many people think that an AI would work something like this: We give a problem to the AI. The AI computes a solution to that problem and then it gives us that answer. That is not what's going on. Despite our use of the term “learning” to describe the process that an AI uses, it actually bears very little resemblance to how humans learn.

For creating an AI, you first need to design an algorithm that contains steps for calculating an “objective function,” namely, a mathematical description of the task. Then, “train” that algorithm on a data set relevant to the task. The result is an abstract mathematical model that represents patterns in the data and an approximate path to the solution. Next, repeat the training process with the right kind of feedback to tune the results. Over many training runs, the algorithm updates the “weights” in its initial mathematical model to get closer to, and eventually converge upon, an optimal solution to the problem. 

Yet it seems to come out with impressive outputs. I once asked ChatGPT: "Write a conversation between Bertie Wooster and Jeeves about a patient with locked-in syndrome watching an ant coming towards him." (I have used the free version of ChatGPT. I hear that the paid version is much better.) Within a second, it gave this output: 

"I had a rather disturbing thought this morning, Jeeves.”

“Indeed, sir?”

“You know how one occasionally sees ants marching about the place? Tiny black chappies with the air of junior solicitors?” 

“Yes, sir.”

“Well, suppose one were lying absolutely still — unable to move a muscle — and an ant were coming toward one across the bedsheet.”

“A most unpleasant hypothetical situation, sir.”

“Not hypothetical, Jeeves. I was reading about this locked-in syndrome. Fellow’s mind perfectly alert, but the body refuses to rally round. Can’t move, can’t speak — only perhaps blink a bit.”

“A grave affliction, sir.”

“And there he lies while this ant approaches. Nearer and nearer. The thing simply keeps coming.”

“Yes, sir.”

I shuddered lightly into the teacup.

“It’s the calm determination of ants that gets me, Jeeves. They don’t hurry. They don’t pause. They advance with the air of creatures who have all eternity before them.”

“Ants are known for persistence, sir.”

“Persistence! That’s the word. If a bulldog advances, one understands the position. There’s drama. But an ant merely toddles onward in a quiet, administrative way.”

“Very true, sir.”

“And the poor chap can’t even twitch his nose. I ask you, Jeeves, what becomes of morale?”

“One imagines it would be severely tested, sir.”

“It made me realise how much of life depends on the ability to perform the occasional meaningless movement. Scratching the ear. Adjusting the sock. Flicking away insects. Civilization rests on these things.”

“A compelling theory, sir.”

“I mean, if an aunt approaches, one can generally escape.”

“An ant, sir.”

“That’s what I said.”

“You said ‘aunt,’ sir.”

“Did I? Freudian slip. Aunts can also produce paralysis of a kind.”

“Undoubtedly, sir.”

I brooded for a moment.

“Do you suppose, Jeeves, that the ant knows?”

“The ant, sir?”

“That the fellow is helpless.”

“I would not credit the insect with such psychological sophistication, sir.”

“No, perhaps not. Still, there’s something rather cosmic about it all. Man trapped in his own body. Ant advancing across the sheet. Silence. Mortality. The whole bag of tricks.”

“Yes, sir.”

“One could almost write poetry about it.”

“Possibly not before breakfast, sir.”

“Quite right. The mind should not grapple with the infinite on an empty stomach.”

It is easy to get amazed by getting such an output in no time. Large language models (LLMs) like ChatGPT are best understood as machines that learn structural patterns that let them produce coherent text. These systems can perform computations on the world’s data far faster than we can. They can create convincing stories because meaningful language has deep structural regularities. They use mathematical data structures to mimic the outputs of human intelligence — our acts of reasoning, speech, movement, sensing, and so on but they can’t understand it. The data storage facility has more in common with a file cabinet than with a living, feeling body. 

It can’t solve a problem unless the solution is somehow embedded in the language data it is trained on. AI tools today lack any idea of what their data represent. It is bits stored on the server. These ones and zeros don’t organize themselves into the intelligent awareness of a complex, ever changing world. A data set, even the entire corpus of the Internet, is not a world. It’s just a digital record of measurements that humans have taken of the world at some point in the past. 

Sam Altman's comment - II

Sam Altman makes it sound as if the training of AI is an independent activity that has no connection with humans.  But one of the factors that has powered the technology’s dramatic progress over the last few years has been the explosion of available data on human behaviour and creativity. A few major well-placed players extract value from other people’s creative work, personal data, or labor. Articles, blogposts, books, paintings, You Tube videos ... practically the entire internet is used as the training data to train the chatbots. 

These tools do a lot of copying of training data. A language model outputs a paragraph that describes an interesting idea with no link to the source(s) it was drawn from. Sometimes a system outputs large amounts of text verbatim or images identical to ones in their training data. The New York Times has brought a copyright lawsuit against OpenAI that features exhibit after exhibit of text, generated from ChatGPT prompts but outputting verbatim text from the newspaper. 

Artists have sued AI companies stealing their work and having people use tools from these companies to pass off AI-generated art as their own. Others, such as author George R. R. Martin of Game of Thrones fame and novelist Jodi Picoult, among others, have also filed suit against OpenAI and Meta for copyright infringement for using their books to train language models. Whatever value AI has is due to the creativity of the human workers who produced the original art, science, or journalism that was used as training data. Artists are being replaced by the very AI models that were built from their work without their consent or compensation. 

AI always involves people. In November 2023, the self-driving car company Cruise admitted that its “driverless” robotaxis were monitored and controlled (as needed) by remote workers. The New York Times published a story that reported that these cars “frequently” had to be assisted by remote human workers. Calling this “misinformation”, Cruise CEO Kyle Vogt clarified: "these cars didn’t need to be remotely driven “frequently,” but 2–4 percent of the time in “tricky situations.” 

Most AI tools require a huge amount of hidden labor to make them work. These kinds of workers do a host of tasks. They are asked to draw green highlighting boxes around objects in images coming from the camera feeds of self-driving cars; rate how incoherent, helpful, or offensive the existing responses from language models are; label whether social media posts include hate speech or violent threats; and determine whether people in sexually provocative videos are minors. 

These workers handle a great deal of toxic content. Given that chatbots recombine internet content into plausible-sounding text and legible images, companies require a screening process to prevent their users from seeing the worst of the web. OpenAI had subcontracted Kenyan workers making less than two dollars a day to filter out gore, hate speech, child sexual abuse material, and pornographic images from ChatGPT and OpenAI’s image generation tool DALL-E. Those workers were lured in by the prospect of breaking into the lucrative field of computing, but ended up with PTSD. 

OpenAI also employed over a thousand other contractors globally to perform reinforcement learning from human feedback on its language models, including prompting the models repeatedly and scoring the answers, in an effort to make the model give appropriate and inoffensive answers.

The generative AI rush has created the “red-teamer”. Red-teaming is a strategy of feeding provocative input of language or text-to-image models, and assessing whether the outputs are biased or offensive. For a model to reach general release to the public, it is the full-time job of multiple people to hurl slurs, violent descriptions, and all manners of internet filth at the model to see if it produces words that are worse, or responds with something morally appropriate. 

They must then deal with potential hateful material in model responses and report them as such. There are people who do this all day long for almost every commercial language and text-to-image model. This takes an immense mental toll on these workers, being subjected to hours of psychological harm everyday. This work is also highly precarious, with tech companies largely directing when and where there will be more work. 

The ImageNet project is a large visual database containing more than 14 million images designed for use in visual object recognition software research. It’s creation would not have been possible if it weren’t for the development of a new technology: Amazon’s Mechanical Turk, a system for the buying and selling of labor for performing small sets of online tasks. It took two and a half years and nearly 50,000 workers across 167 countries to create the dataset. 

This industry has been called by many names: “crowdwork”, “data labor”, or “ghost work” as the labor often goes unseen by consumers. But this work is very visible for those who perform it. We wouldn’t have the current wave of “AI” if it weren’t for the availability of on-demand laborers who could be called upon at any time to perform a set of tasks whenever some AI researchers or corporate engineers demanded it. The critic Astrid Taylor calls it faux-tomation, or fake automation. In this sense, we are certainly used to being told that systems are intelligent and almost see magic, but actually are being propped up by exactly this type of click work.            

Then there is also the labor involved throughout the supply chain of mining, construction, transportation and installation of the hardware. The philosophers Michael Hart and Antonio Negri have a phrase that captures this nicely - The dual operation of abstraction and extraction. Abstracting away the material conditions of production while extracting ever more information and resources. It seems magical from the front end because we never see the deep costs. 

Sam Altman's comment - I

During the AI summit in India, the OpenAI boss, Sam Altman, tried to ease concerns about how much power is used by artificial intelligence models. He told the Indian Express: “People talk about how much energy it takes to train an AI model – but it also takes a lot of energy to train a human. It takes about 20 years of life – and all the food you consume during that time – before you become smart.”

First let us take a look at who Sam Altman is. He is a product of Silicon Valley. His career was first as a founder of a startup, and then as the president of Y Combinator (YC), which is one of the most famous startup accelerators in Silicon Valley, and then the CEO of OpenAI. He is incredibly good at telling stories about the future and painting these sweeping visions that investors and employees want to be a part of. 

He tried to build a portfolio of different investments and different initiatives to place himself in the center of various trends, depending on which one took off. He invested in quantum computing, in nuclear fusion, in self-driving cars and he developed a fundamental AI research lab. Early on at YC, he came to the conclusion that AI would be one of the trends that could take off.

Ultimately, the AI research lab was the one that started accelerating really quickly. So he made himself the CEO of that company. Originally, he started it as a nonprofit to try and position it as a counter to for-profit incentives in Silicon Valley. But within one and a half years, OpenAI's executives concluded that if they wanted to be the lead in this space, they had to go for a scale at all costs approach. Apparently, there are actually many other ways to have progress in AI that does not take this approach.

But once they decided on this approach, they realized that the bottleneck was capital. It just so happens that Sam Altman is a once-in-a-generation fundraising talent. He created a new structure, nesting a for-profit arm within the nonprofit to become a fundraising vehicle for the tens of billions and ultimately hundreds of billions that they needed to pursue the approach that they had decided on. 

He is extremely good at understanding human psychology and then motivating people to do what he wants them to do.  He is able to wear different hats depending on who he's talking to. He pushes a certain narrative when he's talking to the government and he pushes another narrative when he's talking to podcasters or journalists. He figures out what it is that his audience needs to hear to get them to then come along with him to the next step. That's why he's been such a successful fundraiser, even when the financial picture of this company is rather bleak.

In Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI, Karen Hao paints a much more critical portrait of Sam Altman than the standard “visionary founder” narrative. He is someone who genuinely believes in the transformative (and potentially dangerous) power of AI. He talks about existential risk and the need for caution, yet simultaneously pushes aggressive scaling and deployment. He seeks to concentrate power in a small set of institutions (like OpenAI and its partners), with himself positioned as a central gatekeeper of the future.

She opened the book with a quote which encapsulates in a nutshell his character and the role that he has played in creating the mythology around AI, AGI and OpenAI: “Successful people create companies. More successful people create countries. The most successful people create religions.”

In her book, Hao describes the difference in opinion within OpenAI between Boomers (AI accelerationists) and Doomers (AI safety advocates). Many Doomers were skeptical about having Sam Altman as the leader of the company that brings Artificial General Intelligence (AGI) because he seemed in too much of a hurry to bring his products to market without paying adequate attention to safety and alignment concerns. AGI could give the company that develops it ultimate power and many employees felt Altman wasn’t the right leader.

Hao says that these tensions were often fueled by Altman’s untrustworthy character. Altman would listen carefully to people to understand what they wanted and would seem to agree with their views. Then he would do the same for others with opposing views. He preferred one to one meetings so employees would learn about this when they talked to each other and they were unsure of what exactly his views were. For those who opposed his agenda, he would quietly work behind the scenes to eliminate them from the company.

He has a younger sister, Annie, from whom he is estranged. She had psychological problems and even did sex work to support herself when Altman was living in million dollar homes. She has even alleged childhood sexual molestation by Altman. Hao writes: 

Annie’s story deepens the dueling portraits that people paint of Sam. He is at once generous and self-serving, agreeable and threatening, a benefactor for so many people and the source of great personal pain for others. Someone who projects sincerity and altruism in public but reveals a more complicated calculus through his behaviors behind closed doors. 

Someone who can give and take away, leaving many with an impression that they are part of a larger game of chess for which only he can see the full board, and the end game is to preserve his power as king.

Delimitation - II of II

This freezing of parliamentary seats according to the 1971 census has several consequences:

1.  India no longer adheres to the principle of “one person, one vote”. One aspect of the “one person, one vote” concept was about granting every single Indian over 18 the right to vote in elections. But for this idea to be meaningful, constituency sizes must be roughly equal. The random circumstance of being born in Bihar means that the constituency size is about 3.1 million, but if the same person is born in or moves to Kerala, the value of their vote increases because the constituency size is 1.75 million.
2. The overall population growth has meant that all Indians are underrepresented (though not equally so) because the Indian constituencies are too large. Currently, across India, the average MP represents 2.5 million people. The size of each constituency is too large compared to other countries and compared to the original Indian Constitution, which capped the ratio at one MP per 750,000.
3. Poorer regions experienced a fall in fertility rates later than relatively richer regions. Poorer Indians are trapped in regions that have higher malapportionment, and therefore, are underrepresented in Parliament.
4.  The states with larger average constituency sizes have a larger share of the population below 25. These states are in the poorer regions where fertility rates fell later. These states therefore have more young people which means that youth are underrepresented in Parliament, and this problem will only worsen.
5.  SC/ST fertility rates are both higher and dropped later compared to other groups. Seats are reserved for SC/ST groups in each state based on the population share of SC/STs in the given state. Now, the SC/ST groups are estimated to be 4 seats short in the Lok Sabha, relative to their population in the states.
6.  Another group affected by the delimitation freeze are Muslims, as Muslim fertility rates are higher and declined later than other religious groups. 

Most see delimitation as a nuisance, a problem that cannot be resolved, and they offer no better solution than to push it back by another 25 years, as was done in 2001. Many state governments, particularly regional parties in southern Indian states, have repeatedly expressed their opposition to any attempt at changing the existing proportions of Lok Sabha seats. Most recently, Telangana IT Minister KT Rama Rao said that southern states must not be penalised for “controlling their population growth and concentrating on development.”

Though most politically palatable,  this “delimitation is best avoided” framing is problematic since it goes against the basic tenet of parliamentary democracy of 'one person one vote'. The longer the process drags on, the more pain will eventually be felt. Had India reallocated seats after each decennial census, the composition of the Lok Sabha would have changed gradually over time. After decades of avoiding the hard decision, any future reapportionment will inevitably induce abrupt changes in the balance of political power. 

If the Indian Parliament doesn’t postpone dealing with the issue again, the problem will require a permanent solution in 2031. One option is to return to the original constitutional ratio of one MP per 750,000, in which case the Lok Sabha would need to expand to 1,872 seats which seems excessive. 

But expanding the size of the house may be more politically feasible than reapportioning the current number of seats. After all, representatives tend to object to any arrangement that takes seats away from their state (which potentially places their own job on the chopping block) but may be less opposed to adding more seats. Another option that has been suggested is that the total number of seats in the Lok Sabha increases such that no state loses its current number of electoral seats. (As of today, the Lok Sabha has a maximum of 545 representatives filling these seats.)

To achieve this without malapportionment, the total number of seats in Lok Sabha would need to be 848 by 2026. (However, it’s important to note that the states would lose proportional share/power in Lok Sabha based on the change in demographics since 1971.) Under this proposal, Uttar Pradesh would have a whopping 143 seats, while Kerala’s parliamentary delegation of 20 would remain unchanged. This would exceed the maximum strength of any lower house or unicameral body in a democratic country today, the highest currently being the UK with 650 seats in the lower house. 

Unsurprisingly, reapportionment carries profound implications for political parties. Parties with bases concentrated in fast-growing northern states — like Bharatiya Janata Party (BJP) — would gain power at the expense of southern regional heavyweights. Whatever formula is adopted, there will be a lot of people in India who will be unhappy about this issue in 2031.

Delimitation - I of II

Article 81 of the Indian Constitution requires that for the Lok Sabha, seats are allocated in a way “that the ratio between that number and the population of the state is, so far as practicable, the same for all states.” And since populations grow, and not evenly across all constituencies, Article 82 provided for redistricting based on the numbers from each census which takes place every ten years.

As a result of this stipulation, the number of constituencies, their size in each state, and their boundaries are determined periodically, an exercise known as delimitation. Delimitation Commissions, separate from an Election Commission that conducts elections, are set up to study how the country’s demographics are changing, based on census data. This decides how many new constituencies need to be added/subtracted in a given state, and/or how their boundaries need to be changed.

This system worked reasonably well in the first two decades post-independence. Then problems started creeping in. The Forty-Second Amendment to the Indian Constitution in 1976 froze the number and boundary of constituencies in the Lok Sabha according to the population numbers from the 1971 census. The freeze was fixed for a period of 25 years, until the 2001 census. When the time came to revisit the issue in 2001, the Vajpayee government brought in the Eighty-Fourth Amendment which postponed the decision until the publication of the census figures after 2026 (which is expected in 2031).

The reason for this freeze initially was uneven population growth. The politicians from southern states of India claimed that they more strictly and successfully followed the Union government’s population control mandate compared to the northern states. As a consequence, they alleged, that they were electorally and politically penalized for complying with the Union government mandate. The Vajpayee government postponed the revision due to the fragile nature of the coalition. 

But the actual issue was not about population or people; it was about money. The Indian system operates primarily through intergovernmental transfers managed by the Union government. There’s considerable variation among the states on their fiscal dependence on the Union government, largely based on the variation in states’ gross domestic product (GSDP) per capita. Even after intergovernmental transfers from the Union government, low-income states spend less than high-income states. But high-income states don’t enjoy all the revenue that is raised off the income and productivity of those states.

The southern states, with wealthier residents, contributed more to the collective Indian revenue pool. The Union government redistributed resources based on need, and the poorer states, with higher fertility rates and therefore higher population and population growth, received a much larger share of the revenue than they generated within the state. The liberalization of the economy since 1991 led to a higher growth rate for all states, but not at the same rate. The southern and western states grew faster, and coupled with the drop in fertility rates,  difference from the northern states have become even more stark since 2001.

The asymmetry between the shares of electoral constituencies relative to the shares of the population for the state is known as malapportionment. After 50 years of dilly-dallying, we are now in a situation where a registered voter in UP is most underrepresented (one seat per 30 lakh registered voters in 2019) while a registered voter in Tamil Nadu is most overrepresented (one seat per 18 lakh registered voters). Interestingly, a study indicates that there were more actual voters per constituency in TN than in UP on average in 2014. It perhaps indicates that a large number of registered voters in UP have migrated outside their constituencies but still remain registered there.

At present, Indian parliamentarians answer to vastly larger sums of people than their counterparts in literally every other democracy: Indian MPs represent an average of 2.5 million citizens - over three times the number represented by members of the House of Representatives in the United States, which ranks second. For example, in Bihar, one Member of Parliament (MP) represents approximately 3.1 million citizens and an Uttar Pradesh MP represents approximately 2.96 million citizens. At the other end of the spectrum, a Tamil Nadu MP represents approximately 1.97 million citizens and a Kerala MP represents approximately 1.75 million citizens.

Tamil Nadu has nine seats more and Kerala has six seats more than what would have been the number of seats if it had been allocated according to their population proportion. While Bihar and Uttar Pradesh, respectively, have nine seats and twelve seats less than their population proportion. By 2031, when the delimitation freeze ends, the problem will only intensify.