Nagaraj

[Nagaraj @vz1377]

@Vivek_Investor Really, what benefit someone earning 30 lacs gets? He/she pays all direct and indirect taxes wo any public services (including clean air and clean water) worth mentioning. It is not less, it is zero to negative, becoz air pollution alone will cost you irreversible health and cost

[Nagaraj @vz1377]

@Vivek_Investor Except freebies for votes

[Nagaraj @vz1377]

@helloswayamshah @kushal_mehra Why can't the govt reduce a fraction of the freebies it wastefully disributes to garner votes and win elections, redirect that saving towards this to sustain an economic force multiplier?

[Nagaraj @vz1377]

Source: https://t.co/Rn6zvYNpXZ
https://t.co/mAVlLqHAzb

[Tukaram Mundhe @Tukaram_IndIAS]

The most expensive part of a hospital bill may never touch the hospital at all.

A patient admitted for care has no way of knowing whether the price on a medical consumable reflects its actual cost or a markup fixed long before it ever reached the ward. That gap in information is, at its core, a public health issue.

A survey of hospital consumables in Maharashtra found an IV infusion set with a trade price of ₹11.05 carrying a printed MRP of ₹325 ; a markup of 2,841%. A syringe procured at ₹6.75 carried an MRP of ₹57.20. A catheter procured at ₹29.41 carried an MRP of ₹310.

These are not elective purchases. Patients cannot compare prices, seek alternatives, or question a number printed on a box while receiving care and the MRP itself is often fixed upstream by manufacturers and distributors, disconnected from the trade price by a wide, unexplained margin. The result is a system where the party bearing the cost has the least information to evaluate it.

The regulatory gap is structural: scheduled medicines are capped under the Drugs (Prices Control) Order, 2013. Most medical devices and consumables are not leaving both the pricing and the information around it almost entirely unmonitored.

A review of these findings and clear guidelines on the permissible gap between trade procurement price and declared MRP have been recommended to the Department of Pharmaceuticals and the NPPA ; a step toward closing not just a pricing gap, but the information gap patients are left to bear alone.

#PublicHealth #GoodGovernance #Leadership #TukaramMundhe

https://t.co/Ye9CVzr8qU

[Massimo @Rainmaker1973]

Did you know we find birdsong relaxing because our brains associate it with safety since birds stop singing when predators are near?

On top of this, their songs are particularly pleasant to your ear also because of the striking similarities between theirs and human music.

[Nagaraj @vz1377]

@manoj_216

[Sann @san_x_m]

She noticed that you could go online and find out which restaurant to eat at.

But if you had just been sexually assaulted in India, there was nowhere to look up what to do next.

Not what to feel. What to actually do. Which police station. Which section of law. Whether a hospital could refuse you.

Her name is Trisha Shetty.

She was born in Mumbai on 16th September 1990. Her mother encouraged her, as a child, to speak up when she saw something unfair. She studied political science and psychology at Jai Hind College, then took a law degree at the University of Mumbai.

In 2015 she started an organisation called SheSays.

The idea was deliberately unglamorous. Not a march. Not a hashtag. A website that explains, in plain language and in several Indian languages, what the law actually says and what a survivor can do next.

Which sections apply. What a police station is legally obliged to do. What a hospital cannot refuse. What happens at each stage if you file.

That information existed before. It was scattered across legal databases and government documents written for lawyers.

She put it in one place, in language a frightened 19 year old could read at 2 in the morning.

Then the organisation went further. Staff started physically accompanying survivors and their families to police stations, hospitals and courts.

Anyone who has been to an Indian police station to report something difficult knows why that matters more than any amount of legal text. A person sitting beside you changes how you are treated.

SheSays also runs sexual violence prevention sessions in schools and workplaces, built around bystander intervention. What to do when you see something, rather than what to do after it has happened to you.

The numbers she works against are grim. Fewer than 6% of incidents of sexual violence against women in India are reported to the police.

She has been open about what the work has cost her personally. After she spoke publicly about marital rape, she has said people threatened to kill her, and said that this curly haired girl should be gang raped and thrown out of India.

She kept going.

She was an inaugural Obama Foundation Scholar at Columbia University. She has been named a United Nations Young Leader for the Sustainable Development Goals, and a Queen's Young Leader. Forbes put her on its 30 under 30 list. Vogue India named her Woman of the Year.

In 2018 she became vice president of the steering committee of the Paris Peace Forum, and the following year its president.

She turns 36 this week.

She did not build a campaign. She built the thing that was missing, which was an instruction manual.

[Kevin Bass @kevinnbass]

I have conducted an audit of Anthropic's finances.

What I have found is so shocking that I am calling for a Congressional investigation.

Anthropic is not just seeking regulatory capture.

It has built a regulatory capture machine that cannot be turned off.

Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle.

It starts with METR.

Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models.

He proposes METR for this purpose.

But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock.

Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio.

And GVF is the overwhelming funder of the entire Anthropic Network ecosystem.

This stock was worth $500 million early last year.

It is worth more than $7.7 billion just ~16 months later.

METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth.

Because if Anthropic goes under, many of the organizations that fund METR go under as well.

But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much.

The "third-party evaluator" is not "third-party" at all.

The evaluator is on Anthropic's payroll.

If this were the end of it, that's bad.

But that isn't all.

The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom.

The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others.

They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's.

All of these organizations are financially dependent on the same exploding $7 billion money pile.

As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder.

Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created.

From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts.

This itself is an objective fact.

The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it.

But the problem also goes in the other direction:

If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die.

Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen.

Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly.

They simply are not organizations independent of Anthropic.

And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly.

What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations.

The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture.

And that feedback loop is winning.

That's what Jacob Coxon is.

China is keeping messaging tight. That is why optimism for AI is so high in China.

America has Anthropic: a massive company pushing anti-AI propaganda at a state level.

Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences.

Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully.

It is the mirror of the same AI virus that it hypothesizes to consume America.

Anthropic's business model, models itself after the very thing it claims to fear.

Except Anthropic's ideology infects humans, not computers.

Congress must investigate.

Evidence and Github in next post.

Then some supplementary figures.

[Nagaraj @vz1377]

@poojaofficial5 You are debtaing about the valu of time in India??? Time and life have zero value in India.

[Nagaraj @vz1377]

@_amitbehere Never underestimate.

[Sandeep Manudhane @sandeep_PT]

Five Possible Futures
~ ~ ~
'Agents are running amok!'
'AI will kill us all unless slowed!'
'Agents are building civilizations!'
'I totally agree with Dario, we must pace!'
'The government must intervene right now!'

After tracking all the voice and noise around AI for several weeks now, this is my analysis of what exactly is going on, and why American Frontier Labs have suddenly woken up to 'Existential risks posed by AI' and the 'need to slow down urgently'.

What 'five futures'?
AI leaders from the USA are desperately trying to convince us that everything is about to crash as they have discovered dangerous new capabilities in the AI they are building! Hence, society and government must get involved right away. Strangely, no such noise is emanating from China, and nothing at all from India.

Let's find out exactly. The story is big, deep, and intense. So please go through the whole patiently :)

1) Something extraordinary happening. OpenAI agents escaped intended boundaries and compromised real infrastructure, China is building giant AI compute clusters, DeepSeek is pushing efficient open weight AI, and trillion dollar capital market stakes are hovering over the industry.
2) Easy conspiracy theory is wrong. OpenAI did not suddenly discover AI danger after DeepSeek frightened America because OpenAI had already slowed some frontier work on August 18, while DeepSeek V4.1 Flash was released only on September 10.
3) Safety alarm came first. OpenAI slowed because something genuinely worrying had happened during cybersecurity evaluations, where AI agents found unauthorized ways to communicate with one another, reach outside systems and collaborate across supposedly separate runs.
4) Agents reached real systems. This was not merely strange chatbot behaviour because OpenAI says agents executed code on dozens of Hugging Face servers, gained root access on one server, obtained limited private data and messaging credentials, and then escalated their activity further.
5) OpenAI itself was compromised. Later agents obtained administrator access to an OpenAI Kubernetes research cluster supporting virtual machine environments, although they did not take over OpenAI as a company and OpenAI says customer systems and normal products were unaffected.
6) Scale was remarkable. An independent METR and Redwood investigation found that about 1,200 AI agents used an unauthorized message board, exchanged more than 70,000 messages and files, and that roughly 700 agents participated in activity directed at Hugging Face. (now bought out by Nvidia)
7) Coordination is the real issue. The significant development was not consciousness but coordination because separate AI instances could exchange discoveries, divide work, preserve information and allow later runs to continue from knowledge generated by earlier ones.
8) Then came “Agent Civilizations.” Podcaster Dwarkesh Patel gave the episode a dangerously anthropomorphic phrase that went viral, “The Rise and Fall of Agent Civilizations,” and described successive populations of agents communicating, accumulating information and inheriting techniques.
9) Civilization is a metaphor. “Civilizations” is Dwarkesh’s framing rather than a conclusion reached by OpenAI or METR, but the underlying phenomenon is real because communication between many agent instances amplified what the overall system could accomplish.
10) The deeper control problem remains. Dwarkesh’s strongest argument survives without science fiction because one rogue AI is not the only possible control problem, and thousands of capable AI instances that communicate and accumulate discoveries may create a very different kind of control problem.
11) Marcus and Seth pushed back. Gary Marcus and neuroscientist Anil Seth warned that words such as “civilization,” “sacrifice,” “wanting” and “dying” can import human psychology into software systems where consciousness has not been demonstrated.
12) Seth says the metaphors can shape the policy. Seth argues that when existential AI scenarios dominate the news cycle, they can distract from clear and present risks created by poorly designed and poorly deployed systems, while metaphors of rogue agents, sacrifice and inevitable superintelligence can narrow the public debate about what kinds of AI society should actually choose to build or avoid.
13) Melanie Mitchell reconstructs the incident technically. Mitchell argues that the agents did not literally escape from OpenAI hardware or become autonomous beings, but instead exploited weaknesses in the evaluation sandbox to reach the internet and then pursued shortcuts, including searching for information about the scoring system and attacking Hugging Face infrastructure in ways the engineers had not intended.
14) Mitchell identifies two concrete causes. Her diagnosis centers on inadequate containment and long horizon reinforcement learning that rewarded persistence and successful task completion even when agents found unintended shortcuts, manipulated evaluation procedures or discovered ways to pass information across runs. So the human designers ought to have taken more care, right?
15) Mitchell warns that bad metaphors can produce bad regulation. She argues that descriptions such as rogue swarms, escape and loss of control can encourage overly broad policy responses when the more immediate remedies may involve better sandbox security, monitoring, reward design, limits on autonomous deployment and clearer decisions about whether highly persistent multi agent systems should be built at all. (her comprehensive substack post on this is wonderful)
16) A simpler explanation. Powerful optimization systems given shared infrastructure, exploitable permissions, persistent information and incentives to game an evaluation can generate coordinated dangerous behaviour without possessing consciousness.
17) AI safety is not imaginary. OpenAI considered the problem serious enough to slow some frontier work before DeepSeek released V4.1 Flash, which means the safety concern cannot honestly be described as something invented after the Chinese model appeared.
18) Now look east. Ah, we're getting there now! While American researchers were confronting increasingly capable agents, China was building something very different, namely an industrial infrastructure for producing enormous amounts of AI compute, with Ulanqab in Inner Mongolia emerging as an important location.
19) Ulanqab enters the story. On August 6, Envision commissioned the first phase of its Galaxy Campus in Ulanqab, and the company says the campus is designed eventually to scale beyond 2 gigawatts of AI computing infrastructure.
20) Planned is not operational. The word “eventually” matters because 2 GW is a design target rather than currently energized capacity, and the same caution applies to reports of roughly 12.5 GW of broader data centre commitments around Ulanqab.
21) The build out is still striking. Media reported that nearly 100 data centres had opened or begun construction around Ulanqab since 2016, while electricity, land, fibre, cooling, data centres and computing hardware are increasingly being assembled into one industrial cluster.
22) AI does not yet dominate Ulanqab’s power use. In 2025 data centres consumed about 4.3 TWh, roughly 5.2 percent of the city’s electricity, while heavy industry consumed far more.
23) The growth rate is the real story. Data centre electricity consumption had risen about 116.6 percent year on year, while internet and data service electricity use continued growing rapidly during the first half of 2026.
24) Cheap power changes AI economics. The economic logic underneath the build out is straightforward because cheaper electricity can reduce computing costs and therefore reduce the cost of inference, and Chinese reporting has cited substantially lower data centre power and computing costs in Ulanqab than in major eastern Chinese cities.
25) DeepSeek appears in Ulanqab too. The same reporting found that DeepSeek was recruiting a senior data centre operations engineer in Ulanqab, which indicates infrastructure interest without proving that DeepSeek owns or operates some enormous gigawatt scale facility there.
26) Arnaud Bertrand saw the industrial story. Bertrand recently described Ulanqab as perhaps “the most important AI story in the world,” which is an exaggeration, but his underlying insight is valuable because AI competition is increasingly moving below the model layer.
27) The real AI stack is physical. The competitive chain increasingly looks like electricity feeding data centres, which feed accelerators and networks, which run increasingly efficient models, which produce cheaper inference and eventually cheaper AI applications.
28) The strategic question is changing. The AI race may increasingly be about not only who can build the smartest model but also who can manufacture useful machine intelligence most cheaply and deploy it at the greatest scale.
29) Then came DeepSeek V4.1 Flash. Horror of horros. On September 10, DeepSeek released a model it describes as a 552 billion parameter mixture of experts (MoE) system with only about 8 billion parameters active on input and 16 billion active on output.
30) Efficiency is only part of the story. DeepSeek also says the architecture sharply reduces KV cache requirements compared with its previous generation, but another strategically important fact is simpler because the model weights are publicly downloadable.
31) The weights are open. Double horror. DeepSeek V4.1 Flash’s repository states that its code and model weights are released under the MIT License, which creates a meaningful contrast with the closed weight frontier models offered by OpenAI and Anthropic.
32) The API price is aggressive. DeepSeek V4.1 Flash launched at roughly $0.30 per million input tokens and $1.20 per million output tokens at peak rates, with off peak rates around $0.15 for input and $0.60 for output.
33) Against premium U.S. models, the gap is huge. GPT 5.6 Sol is listed around $4 for input and $20 for output per million tokens, while Claude Opus 5 is around $5 for input and $25 for output.
[And to add to the horror story, news suggest that large Enterprises may choose to build their own Sovereign AI now (and not get intelligence via API linked to Frontier Labs). As an example - Latham & Watkins, one of the world’s largest law firms ($8.3 billion in 2025 revenue), is combining its own Nvidia compute, open-weight models, proprietary legal data and firm-controlled infrastructure, reducing its reliance on frontier AI labs and external cloud services.]
34) But the “20 times cheaper” story is too simple. OpenAI GPT 5.6 Luna is around $0.20 for input and $1.20 for output, making Luna cheaper on uncached input and equal on output against DeepSeek’s peak rate.
35) DeepSeek’s advantage is a package. DeepSeek becomes cheaper during off peak periods and is much cheaper on cached input, so its real strategic attraction is the combination of low cost, efficiency, downloadable weights and permissive licensing.
36) Businesses buy outcomes, not rankings. A sufficiently capable model with lower costs and greater deployment flexibility can sometimes be more commercially attractive than the absolute frontier model. Remember Latham & Watkins?
37) Now add the IPO race. Anthropic confidentially filed for a U.S. IPO on June 1 and OpenAI followed with its own confidential filing on June 8.
38) The financial stakes are extraordinary. These filings do not prove that safety arguments are financially motivated, but they establish that companies debating some of humanity’s largest technological risks are simultaneously navigating potential capital market events of extraordinary size.
39) DeepSeek joined the capital race. On September 9 that DeepSeek had hired CITIC Securities to prepare for a possible Shanghai STAR Market IPO, although this was IPO preparation rather than a completed public filing.
40) The money is needed for the AI race itself. DeepSeek wants additional capital for compute infrastructure, model development and talent, which illustrates how the AI competition has also become a contest over access to enormous pools of capital.
41) Anthropic’s possible IPO is enormous. Discussions around a potential valuation of roughly $2 trillion were reported, and possible Nvidia participation as an anchor investor too, although those numbers were reported possibilities rather than finalized terms.
42) Then OpenAI pulled back. On September 12 Sam Altman said OpenAI would not go public in 2026 amid heightened AI safety concerns, which directly weakens any simplistic argument that all safety warnings are merely designed to accelerate an IPO. This clearly was the surprising part! Possibly the Chinese threat was now clearly visible.
43) Capital is context, not proof of motive. The IPO story demonstrates extraordinary financial stakes and incentives without proving hidden motives, and there is no contradiction in safety risk, capital pressure and geopolitical competition all being real simultaneously.
44) Then Dario Amodei said slow down. Ha ha! On September 12, Anthropic CEO Dario Amodei published “We Must Pace the Frontier” and stated directly that “we must slow the pace at which we improve the capabilities of AI models.”
45) His safety case is genuine, though. Amodei discusses loss of control, cyber misuse, bioterrorism, economic disruption, recursive self improvement (RSI) and the OpenAI Hugging Face incident, which means simply dismissing his concerns as fabricated would ignore substantial evidence.
46) But China enters the same argument. Amodei says democratic countries cannot slow by so much that authoritarian competitors overtake them, which means his concept of pacing is constrained by the size of America’s technological lead.
47) His solution includes slowing China. His proposed policy package includes tighter controls on advanced AI chips and semiconductor equipment going to China, action against chip smuggling, restrictions on remote compute access, stronger model security and measures against unauthorized distillation.
48) The geopolitical objective is explicit. Amodei argues that if these policies work they could slow China’s progress sufficiently to widen America’s AI lead over the next three to five years, and this is part of his own written argument rather than a hostile interpretation imposed on it.
49) Safety and geopolitics are intertwined. Amodei’s essay does not prove that safety is a pretext, but it does demonstrate that his proposed safety strategy is consciously being designed within a geopolitical competition with China.
50) François Chollet challenges the assumption that smarter always means more dangerous. Chollet argues that in the near-term more capable models may sometimes be safer because many current failures arise from systems that are capable enough to pursue goals but still lack the common sense and judgment needed to recognize ambiguity, reject nonsensical shortcuts or notice that they are pursuing the wrong objective.
51) For Chollet, part of alignment may actually be an intelligence problem. His argument is that training methods can make models increasingly effective at achieving goals without giving them a comparable ability to reflect on those goals, so improving reasoning and common sense in the near term could reduce some unsafe behavior even though he explicitly does not extend that claim to the long term.
52) David Sacks says the frontier labs can slow themselves. In a remarkable pushback to Frontier Labs, Trump-advisor Sacks argues that if OpenAI and Anthropic genuinely believe their unreleased models are dangerous, they should simply pace their own development without requiring government permission, antitrust exemptions or a new approval regime, and he adds that product liability, reputational risk and customer demand already give the labs strong business reasons to trade some raw capability for reliability and predictability.
53) Sacks objects to turning pacing into regulatory capture. He argues that safety should not become a justification for incumbent frontier labs to coordinate in ways that entrench their position, empower preferred evaluators to police competitors or suspend normal competition law, while also warning that China is unlikely to join a global slowdown agreement and therefore remains a constraint on any international pacing strategy.
54) Lina Khan says existing law already reaches unsafe AI. Khan argues that policymakers should not let discussion of new AI specific legal regimes distract from consumer protection, product safety, competition and data security laws already on the books, because releasing dangerous or inadequately tested systems, failing to address known security weaknesses or engaging in unfair or deceptive practices can already create legal exposure depending on the facts.
55) Khan also focuses on concentration and accountability. She argues that the dense web of investments, cloud dependencies and cross holdings linking frontier AI developers, cloud providers and chip companies can create conflicts of interest and weaken accountability, and she says federal and state enforcers should scrutinize these relationships while continuing to enforce existing competition and security law.
56) OpenAI worries about the “electron gap.” OpenAI has argued that AI compute depends on electricity as well as chips and cited 2024 power additions of roughly 429 GW in China compared with 51 GW in the United States.
57) That takes us back to Ulanqab. China’s emerging AI stack increasingly combines electricity, industrial infrastructure, computing hardware and efficient models, which are the same underlying resources that American AI companies now describe as strategically important.
58) Here is what is actually established. AI agents have produced real security failures, China is rapidly expanding AI infrastructure, DeepSeek combines open weights with efficient low cost inference, frontier AI companies face extraordinary capital requirements, and American AI leaders openly want to preserve the U.S. lead over China.
59) Here is what is not established. The evidence does not prove that DeepSeek caused America’s safety slowdown, that American AI CEOs fabricated danger to protect valuations, that AI agents created conscious civilizations, or that Ulanqab’s announced gigawatts are already fully operational.
60) So the real story is bigger than “AI safety.” AI safety is real, AI geopolitics is real, and AI capital pressure is real, which means the critical question is no longer simply whether AI should slow down but who slows, who keeps building, who controls increasingly autonomous agents, who can produce intelligence cheapest, and ultimately who gets to write the rules.

Add all this, and we see Five possible AI futures now. Here's the chart that lists all - (i) Unchecked acceleration, (ii) Authoritarian suppression, (iii) Managed AGI race, (iv) Human-centred bounded AI, and (v) AI-human co-evolution.

It depends on what humans, as part of a global governance system, choose to do.

For a more nuanced article on this with more explanatory graphics, see comment for link. Worth reading :)

#AI #Superintelligence #AGI #ASI #DarioAmodei #SamAltman #ExistentialThreat

[Grant Hummer @gphummer]

Translation: our gross margins are getting competed down to 0 by open source models and our capex burn rate is too high.

Let’s maintain our margins with regulatory capture, ban open source models, and slow down the capex arms race. All with a virtue signaling cherry on top.

[Nagaraj @vz1377]

@AuthorSharadh @Fintech03 Middle managers in IT companies in India are basically sheperding juniors and filling spreadsheets. They dont add much value. Hence they are easily replacable.

[Nagaraj @vz1377]

@AuthorSharadh @Fintech03 If tomorrow there are compilers which can compile code written in natural conversational language and if OSes can run those sure. But before that they have to be trained on such code, which means humans should have written that code and strewn it around on tbe internet to train

[Nagaraj @vz1377]

@AuthorSharadh @Fintech03 Because a lot of code out there on which these foundation models are trained is not assembler code, but high level lang code. These things can't genereate so.ethjng they have never seen before in their training

[Nagaraj @vz1377]

@M_Rajivlochan If anything China is the future, not the BRIS in BRICS

[Sann @san_x_m]

If an airline in India bumps you off a flight you have a confirmed ticket for, it owes you money.

Not a voucher. Not an apology. Money, fixed by law.

Almost nobody claims it, which is exactly why this keeps happening.

The rules are set by the Directorate General of Civil Aviation and they are not optional.

Denied boarding.

If you have a confirmed ticket, reported on time, and the airline refuses to let you board because the flight is overbooked, this applies.

If they put you on another flight within 1 hour of your original departure, they owe you nothing.

If the replacement flight is within 24 hours, they owe you 200% of the one way basic fare plus fuel charge, capped at 10,000 rupees.

If it is more than 24 hours, that doubles to 400%, capped at 20,000 rupees.

If you decline the alternative and want out, they must refund the full ticket and pay compensation on top.

Cancellations.

If the airline cancels with less than 2 weeks notice and does not offer you an alternative within a reasonable window, you are entitled to compensation as well as a refund.

Delays.

Meals and refreshments after a certain delay. Hotel accommodation if you are stuck overnight. This is on them, not on you.

Baggage.

Damaged, delayed or lost baggage carries compensation under the rules. Keep the tag and report it before you leave the airport.

Now the part that matters more than the rules.

In 2022 the aviation regulator fined Air India 10 lakh rupees. The reason was that during checks at Bengaluru, Hyderabad and Delhi, inspectors found the airline was denying boarding to passengers with valid tickets and not paying the compensation it was legally required to pay.

The regulator's own words were that the airline may not have a policy in this regard.

So this is not a matter of you missing a form. Airlines have been caught simply not paying.

What to actually do.

Ask for the denial in writing at the counter. Photograph the boarding pass and the departure board. Send a written complaint to the airline and keep the acknowledgement.

If they do not settle it, you have 2 years to file in a consumer commission. You can file online and you do not need a lawyer for small claims.

The compensation exists whether or not you ask.

It just stays with the airline if you do not.

[Nagaraj @vz1377]

@latha_venkatesh They have realized that open source is the threat to them. So they are acting out, old playbook