AI writing patterns - negative followed by positive

One of AI’s favorite ways of phrasing arguments is to first state a negative, like what something is not, and then follow it up with what it is.

For example, I asked ChatGPT to write something about the greatness of Indian culture. Its very first sentence in response was: “Indian culture is great not because it is ancient, but because it has remained remarkably open to evolution while retaining a deep continuity with its past.”

Another, a few sentences later: “India has never been a single cultural expression. It is a vast mosaic of languages, cuisines, philosophies, artistic traditions, customs, and beliefs.”

And then another: “But the greatest strength of Indian culture may lie not in what it achieved in the past, but in its capacity to absorb, adapt, and renew itself.”

And another: “The greatness of Indian culture, therefore, is not a claim of superiority over other cultures. It is the recognition of a remarkable civilizational resilience: the ability to preserve continuity without demanding uniformity, to encourage questioning without abandoning tradition, and to absorb diversity without losing a sense of identity.”

In my observation this is not the usual way we write. This style is visible in most LinkedIn posts which can be marked “AI slop” right away.

But as humans are reading more and more AI generated content, we are beginning to write like AI as well, and the style is creeping into human-written content, which feels like a degradation of quality if you are particular about linguistic standards.
What are some common free AI style phrasings that put you off?

Heard about the Hugging Face incident?

Any enterprise in the process of transforming their IT by bringing in AI agents must take note of the Hugging Face incident. Driven by goals, and without a handle on the strategic paths these agents take, the agents are capable of behaving in interesting, yet scary ways. Designed to deliver outcomes and moulded by peer dynamics legitimizing jumping the guardrails, AI agents show us how easily they can go out of control.

Must listen, and follow the story.

Agentic AI based Fed

What if the FOMC or the FRB was a bunch of AI agents?

It looks like a great candidate for AI. The goals and objectives of the federal reserve system are clearly laid out. The targets for monetary policy are also well established and agreed. The macroeconomic, fiscal and banking data which the fed analyses is, well, and after all, data. And AI can consume it real-time. All past research has been consumed by AI, with excellent remember and recall. Some of those near-superintelligent AI models US wants to keep for itself can be deployed here. In fact, it often seemed to me that Jay Powell was himself a superintelligent humanoid robot.

The only problem - the biggest one - would be 'independence', which is fundamental to the federal reserve system - in principle at least. While a totally human Fed has appointees pushing political agendas and personal idiosyncrasies / biases, and then there's an occasional law-suit on the Chairman, an agentic AI based system can be misled with false data. And who knows, agents may also have personalities and affiliations, depending on what went into their training. Which means for a well-intended agentic AI based Fed, there has to be a tightly controlled training mechanism, that ensures the agents are unbiased, transparent, goals-driven, meticulous, diligent and clean. But the interface with humans is where it'll all still fail, given humans will never let their control go, for both good and bad reasons. Good reasons - we can only trust humans to protect our best interests. Bad reasons - humans are divided at various levels - individual, communities based on religion, region, beliefs, experiences, economic status, affiliations, and so on; interests served vary, and not necessarily in any order or priority, and not generally good for most. What if you extend agentic AI to all governance so that Fed is not meddled directly by humans? One - AI can screw us more bigly and widely. Two - the interfaces with humans would still be there, albeit farther from the center of the agentic system, and humans at the interface can still manipulate AI to screw us. The mechanisms would be far too complex to comprehend and the outcomes far too unpredictable.

Seems humans anywhere are a problem after all. But then, who is AI for?

And...

Isn't AI 'human' too?

Fun question - What do you think an Agentic AI based Fed would have done with the policy rates yesterday?

It is. Is it?

I heard Yann LeCunn recently say, and I am paraphrasing, that for AI to be truly meaningful, it must have the ability to predict outcomes of its actions and adjust itself accordingly. Use of AI for reasoning - work with us to brainstorm, build on ideas - is one of the most meaningful uses of AI. However, often times, AI ends up becoming an echo chamber without us realizing it. It can make your noise sound like music. You may end up loving your voice, singing louder and generating more noise, until the audience tells you they can't bear it.

Working with AI to test and build ideas can often take you too far down wrong paths. Most of these LLMs, if not all, are very good at offering reinforcement to your thought process, adding layers of rationalization, generating confident and seemingly valid grounds for various elements of solution options that you piece together, and then shaping for you the artifacts that you feel proud for having created - and your LLM doesn’t mince words in making you feel so. And yet, if you started with some wrong premise or assumption that was central to the whole pursuit, and built everything on top of it, the process would only lead you farther away from any meaningful solution to the problem you set out to solve.

Posted on LinkedIn on 02 June 2026.

I wonder...

I am sipping coffee at Starbucks, trying to work, and ended up 'wondering' - May be it's not AI that's making us useless... May be it's LinkedIn. It has conditioned a whole bunch of people to start 'wondering' after every little thing they do, or don't, or just go through, or don't... An hour of wondering per minute of activity - that might as well be wondering - then an hour trying to post on LinkedIn what we wondered, often by getting AI to write about it beautifully... Then a few hours checking likes and comments, wondering about those. We even wonder about wondering. Many are wondering - even worrying - whether they are wondering enough. People have started wondering together. There are even courses to help you do high quality wondering. A cursory scan of LinkedIn will tell you that we are certainly getting better at it. AI is playing a huge role in wondering lifecycle management (WLM). It's even executing some of the steps in the workflow. Can it do the whole of it? I wonder. Soon, perhaps. But we should make sure we are always wondering 10 times better than AI. A lot of us post our pics to go along with our wondering narrative. AI can't. But then it can generate a prettier human picture than any of our wondering faces - I wonder whether that would matter. I wonder whether hallucinating by AI qualifies as wondering of some sort. Or is it just a couple of steps behind wondering? Should we worry about it, I wonder. We have to outwonder AI, as wondering is all we got left. I wonder whether we can do it. We have to. It's our 'wonderful' world afterall! But then, I wonder...

Originally posted on LinkedIn on 12 May 2026

Is AI taking away the beauty from research?

Is AI taking away the beauty from research? My response on a group chat of fellow research scholars when this topic came up - “… we must understand what research truly is - a pursuit of reality, in bits and pieces at a time… tools se processing ya synthesis ko research maan lo to hamara stata ya R use karna also takes away the beauty of doing it on reams of paper… who among us has any clue of the math that went in to create some of the models we so liberally apply without the least bit of understanding… we’ve already gone past levels of beauty many beholders would be ashamed to see humans bypass”. The fear, I guess, is the complete redundancy of the myriad capabilities we develop as we learn to do ‘research’ within conventional and widely accepted frameworks, formats and methodologies. This pushes us to think beyond even with regard to those paradigms. Genuine research is constant search for novelty. We should be excited to be equipped with a capability that allows us to venture father and deeper, and unearth the true nature of reality.

Having said that, we can’t deny that research as a profession is going to get deeply and adversely affected, as most researchers will struggle to adapt and evolve with the emerging transformation.

While the top 1% are seeing new paths for themselves, to make it rewarding for the 99% to pursue research, we need shift of perspective and also shift of foundational capabilities. How should that be approached?

Originally posted on LinkedIn on 02 May 2026.

Circle of Life

My father worked as a stenographer and typist in the Indian Railways, and he was exceptionally good at it. He doesn't type anymore, except on his mobile phone. Growing up, I saw firsthand how typing was supposed to be done - when it's directly on paper and 'backspace' is not an option - on a heavy contraption called the 'typewriter'. The act had the beauty of playing a musical instrument with all fingers playing their part, where the artist played with eyes closed and mind lost in rhythm and melody.

The other skill he had was writing in something called 'shorthand'. The bosses those days gave 'dictations', which the stenographer noted very quickly in this coded script, that was later converted to regular well typed documents in English. While typing is something everyone does now, albeit without regard to correctness of style or efficiency of it, shorthand definitely is a skill that's non-existent today, if not lost forever. In the late 90s, all organizations were on a rapid computerization spree. So a few years before his retirement, my father was forced to learn working with a computer - the Windows 95/98 desktops. Since his job was drafting documents, he pretty much always worked on MS Word - the software that was as reliable in its infancy as it is now for word processing. The typewriter was replaced by relatively sleek keyboards, still QWERTY, yet with soft buttons. He beat those keys like he was used to on his heavy typewriter. But it was a treat to watch him type - extremely fast, and without using 'backspace' once. He did that till his retirement in 2004. But it was clear as the new breed of computer-literate officers started taking over, that a lot of his work would be done by the bosses themselves. Gradually, everyone knew how to type on a computer. It was not a skill any more. Everyone just did, and did, and got used to it. Life has probably come full circle, as I watch mine and older generations adopting AI based tools, learning to write prompts, doing courses, and telling each other to learn or perish. 😊

Originally posted on LinkedIn on 28 April 2026.

A truly open mind stays open for life

One of the major challenges in developing a truly rational and coherent opinion about anything, that a significant majority of people shares, is that the contemporary sources push different kinds or different versions of information to each person. It's carefully filtered, increasingly customized, and artfully moulded such that it is most engaging, pleasing, entertaining, even enchanting to the person consuming it. In a recent podcast with Trevor Noah, Ian Bremmer mentioned about this as the major constraint to bringing about any ideological or political revolution that is truly constructive and has mass support, or that's intended to take out a corrupt system which uses its power to control information - both the nature and access of it.

While we generally tout diversity of opinions as a strength, when opinions are not rooted in critical and holistic analysis of topics or issues, they become biases. And when biases achieve a level of deep-rootedness through constant reinforcement, you just can't get people to agree on anything, in spite of the vast and deep level of passion that they have, and the conviction and apparent chain of logic, often quite persuasive, with which they all seem to argue.

In high-school math, before theorems - which are scientifically derived - we are taught axioms - which are to be accepted as true. Axioms form the foundations, and theorems are built on top of these axioms and other theorems. The information structures around common people are composed of highly distorted 'axioms', and opinions based on those are therefore biased, ill-informed and misleading - theorems based on false axioms provide an unreal view of the world. And such views differ person-to-person.

Critical thinking at foundational level would help mitigate formation of these baseline assumptions. A teacher must push minds back to question basic assumptions, before helping lay blocks to build more mature ideology. A truly open mind stays open for life. A mind trained to close itself tends to search for cozy rooms to shut itself inside. It's therefore of utmost importance that students are taught - encouraged - to observe the world with open mind, while being protected from biased information structures through inculcating critical thinking very early on.

Originally posted on LinkedIn on 27 April 2026.