Date: 12th Nov. 2026
To,
- All members of Pensioners' units affiliated to AIRIEF
Please find enclosed draft joint letter dated 12th Nov.2026 alongwith a draft joint resolution to be signed by Secretary of the units and to be submitted to ZMs/SDMs after the demonstration.
Please remember to abide by following:
- 1.On top of the Resolution, the names of both the units be mentioned.
- 2.The 2 Secretaries signature and names side by side (not one below the other) alongwith units name, station etc. to be mentioned.
- 3.3 copies of the joint resolution be carried for submission. Submit 2 copies and obtain acknowledgment on 3rd copy.
- 4.It will be in the fitness of the things to have a meeting with our joint action partner and finalise a roadmap including fixing the responsibility of keeping the memorandum. Even if the partners own the responsibility to bring, it is advisable for our units to carry 3 copies to eliminate any act of omission.
- 5.Demonstration to be done during lunch hours.
I am not mentioning time as I understand lunch hours vary all over India.
With this Circular in hand, I hope there remains no ambiguity and action plan is without any loose end. Pl call upon our members to join the demonstration program.
Wishing you all success.
(Ranjan Dey Sarkar) General Secretary, AIRIEF
Encl: Jt. letter dated 12.11.2026 and drafted resolution.
ALL INDIA INSURANCE PENSIONERS’ASSOCIATION & ALL INDIA RETIRED INSURANCE EMPLOYEES’ FEDERATION
12.11.2026.
Dear Comrades,
This is in continuation of our Circular dt. 10.11.2026 advising all the Units to hold demonstration on 18th November, 2026 during lunch recess before DOs/ZOs of LIC demanding solution of pending issues of LIC Pensioners.
We sent a draft resolution to be adopted in the demonstration/gate meeting. In the said draft we made a provision for signature of the President of the meeting. Since the demonstration is conducted jointly by AIIPA &AIRIEF it is only fair to affix signatures of representatives of both the Units in the resolution.
We, therefore, request the Units to hand over resolution adopted to the officer-in-charge after having signed by representatives of both the organisations. A modified draft is attached herewith for the purpose.
Comradely yours,
M Kunhikrishnan, General Secretary, AIIPA.
Ranjan Dey Sarkar, General Secretary, AIRIEF
Resolution.
This meeting of LIC Pensioners of................................................................ (Name of the Division) held at........................... (Place) on 18.11.2026 notes with deep concern and consternation that many of the pressing issues of the insurance pensioners remain unresolved till today despite the fact that these stand represented to the authorities concerned umpteen number of times. The inordinate delay in settling the issues has been causing untold sufferings, monetary and otherwise, to the pensioners apart from denying justice to them. The issues are as follows:
- a)Pension Updation: The most pressing of all the issues that confront the insurance pensioners is the matter of periodical revision of the basic pension following the wage revision of the inservice employees as is in vogue in the case of Central Government Employees. Pension Rules, 1995 of LIC make it abundantly clear that the rules were framed on the basis of Central Civil Services (Pension) Rules. But the improvements that came to Central Civil Service Pensioners as per the recommendations of the Central Pay Commission providing Pension Updation, are being denied unjustly to the insurance pensioners. Besides, such a pension updation scheme was also extended to the RBI pensioners later. But, despite repeated representations, insurance pensioners stand deprived of this benefit. Consequently the Basic Pension of the insurance pensioners continues to remain stagnant. This has created serious distortion, anomaly and discrimination. The management of LIC of India, unfortunately, continue to take the plea that the matter is sub-judice since it is under adjudication before the Supreme Court. But given a positive approach from the LIC management an out-of court settlement of this issue can be made possible.
This meeting strongly urges upon the LIC management to consider the matter with all the urgency, it deserves, and come out with a positive stance on this issue.
- b)Uniform DA neutralisation for pre-August, 1997 pensioners: This is another long pending issue crying for solution. As far back as in November, 2001 LIC Board approved and recommended to the Central Government for full neutralisation of DA for pre-August, 1997 pensioners. But, unfortunately, the matter still remain undecided. This meeting, therefore, urges upon LIC management to resolve the issue in line with the LIC Board resolution.
- c)Substantial increase in ex-gratia to pre-1986 retirees: This is another issue that stand represented to the management several times.This meeting urges upon the LIC management to consider an upward revision of ex-gratia to the above category of retirees,who are super seniors and whose number is very minimal, in line with at least whatever is provided by Union Bank of India, a public sector Bank, to such retirees.
- d)There are other important issues like Cash Medical Benefit for domiciliary treatment, Preventive health check up, Improvements in Mediclaim Scheme including increase in Cadre Sum Insured, Full subsidy in premium for cadre sum insured to all pensioners, Coverage of dental treatment, cost of hearing aids and other age related expenses, full reimbursement of cost of robotic surgery without restricting the benefit to certain procedures only, Withdrawal of GST on the premium of Group Mediclaim schemes, Consideration of apprentice period of Class IIDevelopment Officers to derive qualifying period of service for computing pension, Increase in basic pension when a pensioner reaches the age of 80 and above as is in vogue in the case of Central Government Employees etc. All these issues are waiting for solution. The delay in resolving these issues has made the pensioners extremely restive.
This meeting, therefore, urges upon the management to take steps to arrive at a satisfactory solution of all these issues at the earliest.
Adopted unanimously.
Signature
Signature
4 comments:
A golden age of science is coming AI can do things humans researchers can’t, even in groups. Noah Smith Oct 29, 2026 It’s almost hard to remember, but just a few years ago, a lot of very smart people thought that science was in terminal decline. Respected researchers were writing papers with titles like “Are Ideas Getting Harder to Find?”, “Papers and patents are becoming less disruptive over time”, and “Slowed canonical progress in large fields of science”. In 1996, science writer John Horgan even published a book called The End Of Science: Facing The Limits Of Knowledge In The Twilight Of The Scientific Age.
The fundamental idea was that the Universe only has so many insights to discover, and that we’ve picked the low-hanging fruit. It was the idea that Joseph-Louis Lagrange expressed when he said that “we cannot find more than once a system of the world to establish.” As the big ideas and the simple ideas get mined out, we’re forced to throw ever more brains at the mysteries of nature just to keep scientific progress going at a fraction of its old pace: It looked like science was hitting a wall. Then along came AI.
It’s far too early to tell how much AI will eventually speed up the growth of total factor productivity. But already we can see signs of a speedup in science. Anthropic Founder Jack Clark assembled some of the hopeful data points in a recent lecture: Anthropic is accelerating its investment into drug discovery, and is already announcing some impressive preliminary results: Claude also appears to be enabling rapid progress toward a treatment for food allergies. OpenAI, meanwhile, announced a possible breakthrough treatment for neurodegenerative disease.
Meanwhile, Google Cloud Chief Scientist Pushmeet Kohli has a list of recent scientific advances that have been enabled by its new Gemini model, including breakthroughs in nuclear fusion reactor design, rare disease research, and quantum computing: But as cool as these stories sound, they’re just very minor, preliminary efforts. There are a number of reasons to think that AI is going to propel us into a golden age of scientific discovery. That age might not last forever — AI might still run into diminishing returns eventually. But the next few decades are probably going to be an absolutely amazing time for human science.
(Note: This post is written under the assumption that human civilization survives. A golden age of science could also easily turn into a golden age of bioterrorism, in which case all bets are off.)
The number of “effective researchers” is no longer limited
The most important input into science is scientists themselves. Until this year, that meant human brains — talented, highly trained human brains. The number of trained human brains could be increased by sending more people to grad school, but only to a point — the smartest people tend to be sent into the hardest research fields first, and if you 10x the number of researchers, the average quality will go down. This constraint was already starting to bite, as Ekerdt and Wu argued in 2024:
We argue that a substantial portion of the downward historical trend in U.S. research productivity is a composition effect resulting from self-selection in researcher ability and the dramatic expansion of the research sector…[W]e find that this composition effect has lowered average researcher ability by between 37% and 53%[.]
And of course, low fertility was making the problem worse, because fewer children meant fewer people who were able to become future scientists.
AI will basically eliminate the theory bottleneck entirely — or at least shorten the turnaround time between experiment and theory by an order of magnitude. AI is an absolute beast at math. Recently, an OpenAI model solved some of the most important open problems in computer science, math, and theoretical physics, and no one even batted an eyebrow because that sort of triumph is becoming increasingly commonplace. One of those solutions was a proof that the Standard Model of Particle Physics could be mathematically well-defined — a problem that human theorists had so far been unable to tackle.
Not all research relies equally on theory, but understanding novel experimental results almost always involves some important component of either mathematical or spatial reasoning. That part of the task has now been made much, much faster. And on top of that, science is now open to a greater number of humans who aren’t naturally good at theorizing.
AI can read the whole literature
Much of the work of human scientists involves reading and understanding endless amounts of papers and going to endless conferences and seminars. The purpose of that work is to understand the existing scientific literature. Understanding the literature allows scientists to do the following things:
Come up with new ideas for hypotheses to test
Avoid doing research that someone else has already done
Synthesize approaches from different areas or fields
AI can reduce this effort to almost zero. Consider this excerpt from OpenAI’s blog post about their new research into neurodegenerative disease:
The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning. The agents examined a whole-brain CRISPR atlas…a dataset showing how gene disruptions affect different types of cells across the mouse brain. Each agent investigated a gene disruption, analyzed cellular responses, searched scientific literature, and proposed explanations for the results. Their reports were combined into a reasoning atlas linking genes, cellular effects, relevant publications, and possible biological mechanisms.
The research team estimated that comparable human analysis and literature review, conducted one perturbation at a time, would take months to a year. The agents reproduced findings from human analyses while uncovering additional leads. Their assessments of novelty and scientific value also aligned with judgments from blinded human experts. These findings help prioritize further experiments needed to test the proposed biological mechanisms.
Some of these insights point to new leads for understanding brain disease. [emphasis mine] This is one case in which parallel work by a large number of agents really does make a huge difference:
Not only does instantaneous literature review vastly speed up the research process, but it allows AI to make novel conceptual leaps from combining approaches from wildly different fields of research. AI regularly does this in math research, combining results, approaches, and theorems from far-flung places that no human mathematician would have thought to combine.
It can do it in science, too. Much of scientific innovation, historically, has been “recombinant” innovation — scientists applying insights from one field to another. With AI, essentially all research is now cross-disciplinary — AI can read not just the literature on a particular subject, but the literature on a million other subjects that might have produced useful techniques or insights for the problem at hand.
Superscience
So far, all of the factors I’ve listed are ways that AI can speed up science as it’s already done. I’ve basically described how AI can send gigantic hordes of superhumanly fast, superhumanly well-read, superhumanly mathematically gifted researchers to re-accelerate scientific discovery.
But it’s also possible that AI will enable types of discovery — and ways of understanding the natural world — that no network of humans, no matter how large or how brilliant it was or how long it had to work, would ever be able to discover. In a post back in July, I wrote about how that might work:
What if there are lots of…aspects of the Universe that [are] too complex to understand in terms of simple laws, but not so complex that they just dissolve into unknowable chaos? It’s possible that we can reliably control these complex phenomena with AI, even if we never reduce them to the kind of principles that we can teach a grad student in a textbook…
Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate…
Perhaps social sciences like economics, sociology, and political science obey similarly complex regularities, and AI can help us find them. Perhaps there are physical processes — plasma, or topological materials, or aerial turbulence, etc. — that obey cloud laws instead of chaos?…
[H]uman collaboration is bottlenecked — it’s limited by our ability to intuit patterns at an individual level, and to communicate these patterns from one individual to another. AI, being a computer, just doesn’t have this sort of limitation; it can work with vast, diffuse patterns without having to break them into pieces or simplify them in order to compress them into the tiny pipelines of person-to-person explanation…
If much of the Universe turns out to be ruled by cloud laws, we could be on the precipice of a scientific renaissance.
In order to discover cloud laws, AI will need huge amounts of data about the natural world. AI figured out human speech by assimilating essentially all of the text humans have ever written. If it’s going to figure out hidden patterns in biology, neuroscience, materials science, etc., it’s going to need similarly massive amounts of data from physical instruments.
That data will be far, far more than humans — individually or collectively — would be able to parse, label, characterize, or model. But AI was built to do this. If scientists simply build huge arrays of sensors to measure complex phenomena like the brain, the cell, the economy, etc., they may be able to feed this flood of data to AI directly and bypass human cognitive intermediation entirely. AI would then learn about the world directly, and it might learn things that humans never could.
This “superscience”, if you want to call it that, would actually go beyond the kind of science humans have done heretofore. Many of the technologies it yielded would be beyond human comprehension — indistinguishable from magic. But to the AIs, they would make perfect scientific sense.
In any case, it seems clear that the scientific slowdown that threatened human progress will now be delayed — at least for a while. Unless humans have somehow found most of the actual exploitable regularities in our Universe, we’re about to enter a new golden age of science.
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