Prabha Uncle

 

Prabha uncle the person he was , his straightforwardness, simplicity and warmth he had will always be itched in our memory. I can’t forget how he tried hard to stay calm and adjust  the last few years even though he could not cope with Geetauntys absence from his life and with his health issues. May all of you have the strength to bear the loss. With his deteriorating health it’s a hard reality that this would have relieved him of further distress. You all did the best you could and am sure his soul will depart in peace. Remembering him very fondly and  in our prayers🙏🙏

Am sure his Children have a lot to carry and it’s tough days. Please take care.

Healthy Work Place vs. Toxic Workplace

 Which work place are you in?

Sunni-Shia divide

 Why did the Sunni–Shia divide begin?

The split began in 632 CE, after the death of the Prophet Muhammad.

The disagreement was about who should lead the Muslim community:

Sunnis believed the leader (caliph) should be chosen by consensus.

Shias believed leadership should stay within the Prophet’s family, specifically through Ali ibn Abi Talib, his cousin and son-in-law.

Over time, this political disagreement evolved into theological and cultural differences.

While the split started as a leadership dispute, modern tensions are often about:

 1. Power and Politics

For example:

Iran is majority Shia.

Saudi Arabia is majority Sunni.

Both countries compete for influence in the Middle East.

2. Regional Conflicts

Sunni–Shia divisions have influenced conflicts like:

Iraq after 2003

The Syrian civil war

The Yemen conflict

3. Identity and Governance

In some countries (like Iraq or Bahrain), one sect may form the majority population but not hold political power — leading to tension.

Most Sunnis and Shias live peacefully together.

The tensions become serious mainly when politics, power, and external influence amplify religious differences.

So in summary:

The divide began in 7th-century Arabia.

Today, it is most politically visible in the Middle East.

It is often more about power and geopolitics than everyday religion.

Economics

 Economics is all about demand and supply, profit and loss, needs and production, world has many countries, country has many states, state has many districts, district has many towns, town has many villages.. every person needs something to take or to give.. that's it economics..

Neema....

She was one of the most genuine and jovial souls I have ever known. I never once saw her without a smile — and I have never met someone who carried so much positive energy wherever she went. She supported us not only professionally but personally, always standing by us with encouragement, strength, and kindness.

Strong. Bold. Charming. Truly inspiring — words feel insufficient to describe the remarkable person she was. Her presence lit up every room, and her impact on our lives will never be forgotten.

Heaven has surely gained a beautiful soul. 

May God grant her eternal peace and give strength to her family and loved ones during this difficult time. You will always be remembered and deeply missed Neema Kaniampuram

One of the most inspiring qualities of Neema was her deep, sincere  love for her mother. The way she cared. The way she stood by her. The sacrifices she made quietly, without ever seeking appreciation. She gave up so much of her own comforts and luxuries just to ensure her mum was happy and cared for. That kind of devotion is rare.

Professionally too, Neema carved her own path. Becoming an HR Manager at a young age and achieving so much through her own hard work,  those were her victories. She built her life with determination and strength.

And now… Neema is no more.

With a heavy and shattered heart, I am now to bid farewell to a dear friend, sister and colleague, Neema Kaniampuram, who had held many hands and walked us through difficult days and good times alike. After a brave struggle, she has left us today, early in the morning. She took care to live every moment to its fullest and pushed us to do the same. I wish her eternal peace, joy and beauty, for that is what she often brought into every life you touched. Your face will forever remain etched in our hearts, beautiful and smiling, and you will be loved and cherished just the same🫶✨

Even as I write this, it feels unreal. Some losses leave you speechless. This is one of them.

Neema, you were love.

You were strength.

You were sacrifice.

You were family.

You will live on in our stories, in our summers, in our hearts.

Gone too soon… but never forgotten. 💔

Podcast with Toby Maier and His article in Finance Maga

 

podcasts.captivate.fm/media/4eb4f78c-cab2-46cb-9e70-0325abb41a8f/SCN-Audio-Supply-Chain-Leadership-Across-Africa-Toby-Maier.mp3?utm_content=370427748&utm_medium=social&utm_source=linkedin&hss_channel=lcp-27241635

🎙️ Key Insights on Supply Chain Leadership Across Africa

In a recent episode of Supply Chain Now, Toby Maier shares powerful insights on the evolving supply chain landscape across Africa and the Middle East.

Here are the major takeaways:

🌍 Shifting Global Trade Routes
Geopolitical changes and trade realignments are positioning Africa and the Middle East as increasingly strategic logistics hubs.

🚛 Infrastructure & Connectivity Matter
Resilient supply chains require investment in transport networks, customs modernization, digital systems, and reliable last-mile delivery — especially in diverse African markets.

💊 Healthcare & Life Sciences Focus
Ensuring medicines and vaccines reach underserved communities is a critical priority, requiring temperature-controlled logistics and specialized distribution networks.

💰 Long-Term Investment Commitment
Through DHL, significant investment is being directed toward strengthening end-to-end supply chain capabilities in the region.

🌱 Sustainability as Strategy
Electrification, sustainable aviation fuel, and greener logistics models are no longer optional — they are central to future-ready supply chains.

👥 Local Talent & Leadership Development
Building resilient supply chains in Africa depends on developing local expertise, empowering leaders, and creating long-term career pathways.

🔎 Bottom Line:
Africa is not just a growth market — it is becoming a strategic pillar in global supply chains. Leadership, innovation, and sustainable investment will define the next decade.

#SupplyChain #Leadership #Africa #Logistics #Sustainability #GlobalTrade

An interesting journey in 'The Finance ' magazine.  From CFO to CEO. Changing world scene and priorities. 

Budgets once was a serious yard stick but now is only a rough guidence. Earlier internal factors determined it, but now its more influenced by external factors. Firms are looking at options like Block chains to  swift in international transactions. 

CEO in the football team is the striker, CFO is the defence or goalkeeper. 

Lead

This is an uncomfortable truth that rarely gets spoken.

Formal training doesn’t really get you ready for what leadership truly requires.

After years of working with executives, I keep seeing the same expression. It’s as if their job description left out the toughest parts. Experience teaches these lessons, but it does so slowly and often at an unspoken cost.

Here’s what that preparation often misses:

1/ Your body will react before you have a chance to decide.

When you are under pressure, your body responds before your mind can catch up. By the time you have thought it through, you have already acted. Most leaders realise this only after the fact, if they notice it at all.

2/ The higher your position, the less truth you hear.

Your role creates distance. Honest feedback that could help you most is the hardest to get. Often, you do not even realize what you are missing.

3/ The isolation comes from the structure, not from you.

You can’t talk through every decision with your team. That’s not a failure to connect; it’s just part of the job. Still, your body sees isolation as a threat, and that shapes every decision you make after.

4/ Your mood sets the tone before you even say anything.

The way you handle stress sets the example for your team, often without realising it. They sense your stress before you speak. This is almost always underestimated, and most leaders hesitate to admit it.

5/ Making the right decision can sometimes cost you people’s approval.

You will have information others don’t. You will still have to decide. Some part of you will feel their disapproval as a threat, even when you know you made the right call.

6/ Recovery is not just self-care; it’s essential for how things run.

When your mental energy drops, your decision-making suffers, and this affects everyone and every choice in your organisation. When a leader is worn out, it doesn’t just impact them, it spreads through the whole organization.

These aren’t problems you can fix. They are realities you have to lead through.

Even the best leaders face all of this. They have just stopped pretending they should not, and that’s where real preparation starts.

Which of these have you never actually spoken about?

 

Negotiation Process

 

Advantage:

1. Better conflict resolution
2. Effective communication
3. Relationship building
4. Increased confidence
5. Career Advancement
6. Problem Solving

Application:

1. Business contracts
2. Job offer & Salary Negotiation
3. Sales & Client Interaction
4. Project Management
5. Vendor and Supplier Relationship
6. Merges & Acquisitions

Steps:

1. Preparation
• Define SMART Goals: Party, Min/Mx, Concensus, Power/Interest/Pressure Point
• Determine/Assess situation : Analyse cost structure of supplier, Answer key questions
• Chain of reasoning/Plan argument
• Plan meeting
2. Execution
• Establish relationship
• Intro and confirm agenda
• Agreement recorded: Define problem, create options, schedule breaks, Agreement
3. Documentation
• Contract closure: Formalisation of agreement
• Implementation of agreement
• Better planning for future: Lessons learnt: What went well, what was the problem? 

GEORGE BERNARD SHAW(1856-1950)

 GEORGE BERNARD SHAW(1856-1950)

He refused Nobel Prize money, then won an Oscar at 83. The only person ever to win both—and he laughed at the irony.

November 1925. The Swedish Academy announced that George Bernard Shaw had won the Nobel Prize in Literature.

Most writers would have celebrated.

Shaw was annoyed.

At 69 years old, he was already one of the most famous playwrights in the world. His works—Pygmalion, Man and Superman, Saint Joan—filled theaters across Europe and America.

He was wealthy, celebrated, and completely uninterested in validation from committees.

More than that, Shaw had spent decades criticizing literary prizes. He believed they corrupted art, turned creativity into competition, and reduced genuine work to a contest with judges deciding whose genius ranked highest.

As if passion could be scored like athletics.

So when the Nobel came calling, Shaw had a problem.

He didn't want to insult Sweden or dishonor Alfred Nobel's legacy, but he also refused to compromise his principles.

His solution was characteristically Shaw: accept the honor, refuse the money.

He called the prize "a lifeboat thrown to a swimmer who has already reached the shore in safety."

Translation: Why give recognition to someone who doesn't need it? Why not support struggling artists instead?

The Swedish Academy was stunned. The British government worried about international relations. Friends pressured him to just take the money.

But Shaw held firm on one point: he wouldn't keep a single krona of the 120,000 Swedish kronor prize.

Then he did something extraordinary.

Shaw took the entire prize amount and created the Anglo-Swedish Literary Foundation—an organization dedicated to translating Swedish literature into English.

For decades, that money funded translations that introduced English-speaking readers to Scandinavian authors they would never have discovered otherwise.

Shaw had transformed what could have been personal glory into cultural service.

He didn't want recognition for himself—he wanted Swedish voices to reach new audiences. He wanted to build bridges between worlds.

But the universe wasn't finished with Shaw's prize irony.

Thirteen years later, in 1939, at the 11th Academy Awards ceremony, George Bernard Shaw won an Oscar for Best Adapted Screenplay for the film version of Pygmalion.

(The same play that would later inspire the musical My Fair Lady.)

At 83 years old, Shaw became the only person in history to win both a Nobel Prize and an Academy Award.

The man who'd spent his entire career mocking prizes now held the highest honors in both literature and film.

Shaw reportedly kept the Oscar statuette on his mantle—a winking acknowledgment of his own contradictions.

He'd spent decades insisting prizes were meaningless.

And now he'd collected the two most prestigious awards in the world.

But Shaw's attitude toward recognition revealed something deeper than contradiction.

It was philosophy in action.

He genuinely believed that art served humanity, not artists. That creativity was responsibility, not a path to glory. That recognition could become a prison—trapping creators into repeating safe formulas instead of challenging audiences with uncomfortable truths.

Throughout his long life, Shaw used his platform deliberately.

He advocated for socialism, women's suffrage, vegetarianism (he was vegetarian for over 60 years), spelling reform, and countless progressive causes.

His plays weren't escapism—they were confrontations with Victorian morality, class inequality, and religious hypocrisy.

When asked why he wrote such controversial work, Shaw said:

"My way of joking is to tell the truth. It's the funniest joke in the world."

He meant it. His wit was a weapon. His humor was a delivery system for ideas that made powerful people squirm.

Shaw lived to 94, writing until nearly the end. He died in November 1950, having produced over 60 plays, countless essays, and enough controversy to fill libraries.

Today, Pygmalion alone has been adapted into multiple films and inspired one of musical theater's biggest hits. His plays are still performed worldwide. His sharp wit still cuts through pretension.

But Shaw's Nobel Prize decision matters more than his awards.

Because it reminds us that recognition—fame, prizes, accolades—can become traps.

They can seduce artists into playing it safe. Into chasing approval instead of truth. Into protecting reputations instead of risking everything for honest work.

Shaw refused that trap.

He took the world's most prestigious literary prize and gave the money away to serve literature itself.

He won Hollywood's highest honor and laughed at it.

He spent 94 years proving that principles outlast praise. That conviction matters more than celebrity. That true artists create to challenge the world—not to be celebrated by it.

George Bernard Shaw called the Nobel Prize a lifeboat for someone who'd already reached shore.

He gave away the money to translate Swedish literature into English.

Then he won an Oscar at 83 and became the only person ever to hold both honors.

And he spent his whole life demonstrating that the only prize worth keeping is the courage to speak truth—even when that truth makes the powerful uncomfortable.

Because recognition fades. Statues collect dust.

But conviction? Conviction endures.

Shaw understood what most people spend their lives forgetting:

The real prize isn't approval—it's integrity.

He rejected Nobel money. He won an Oscar. He laughed at them both.

And he left behind a legacy no committee could ever measure.

On Valentine's day 💘 Books

 Cochin Book Club, at MyLibrary.

After a highly enjoyable discussion of the BOTM and other works. On Valentine's day 💘

“For mad is the heart of Love, 

  And gold the gleam of his wing; 

  And all to the spell thereof 

  Bend when he makes his spring. 

   All life that is wild and young

   In mountain and wave and stream 

     All that of earth is sprung, 

     Or breathes in the red sunbeam; 

     Yea, and Mankind.  O'er all a royal throne, 

     Cyprian, Cyprian, is thine alone!”

 

Mahesh

Meet the Savarnas - Indian Millennials Whose Mediocrity Broke Everything by Ravikant Kisana

Vineeth 

The Red Pony by John Steinbeck 

Philip

PG Wodehouse

I mentioned The Reluctant Empress by Brigitte Hamann.

Gopal

Orbital - Samantha Harvey

Satheesh MK 

The Morningside - Téa Obreht 

Bright Ages - Matthew Gabriele and David M. Perry

Tapomayiyude Achan - E Santhosh Kumar

Kali by Aswathy Sreekanth 

Madhavikutty - Pranayakadhakal

Dr. Unnikrishnan 

Waiting for Jonathan Koshy - Murzban Shroff

Nandakishore

Skin Folk - Nalo Hopkinson

In Xanadu by William Dalrymple- Dr. Rajesh

An Odyssey of the Mind - Narendra Murthy - Harris 

Jessica

Grumpy Darling - Alexandra Moody

The Stethoscope Meets the Scalpel - Dr. Jimmy Mathew 

Midnight's Children - Nandita

The Maniac 

Selma 

The Banker Who Refused to Walk Away - Swami Raj 

The Polyester Prince

Nirmala

The Elsewhereans - Jeet Thayil

They Will Shoot You Madam - Harinder Baweja 

Mary 

There Are Rivers in the Sky - Elif Shafak

Sharika

This Life at Play - Girish Karnad

Auggie and Me 

Raji

Dream Count - Adichi

Krishna

Khasakkinte Ithihasam

Against the Gods

The book is “Against the Gods: The remarkable story of Risk”

Unnikrishnan 

Nocturnes - Ishiguro

Censoring an Iranian Love Story

Paulose 

Mushiyaatha Nottukal - C Prakash 

A Kind of Meat and Other Stories 

Jyotsna

Njaanabhaaram - E. Santosh Kumar

[14/02, 22:45] PHILIP ABRAHAM CBC: I can't think of a better way to end Feb 14 than by reading The Courts of Love.

The story of the dauntless and matchless Eleanor of Aquitaine. The only woman to have been married to a Roi de France and a King of England. 

She is said to have presided over the  Courts of Love (or "Courts d'Amour") which  were tribunals where questions of romantic etiquette, courtly love, and chivalric behavior were debated and judged, in 12th-century France.

Probably, not factually true, but when you have seen Queen Kate flash her magnificent eyes at Peter O'Toole on the big screen , you want to believe it really happened !

[14/02, 23:14] Raji Nair CBC: One of my favourite love story is Desiree. It is the story of Napolean's first love and how he marries Josephine for political reasons. The book ends with Desiree becomes the queen of Sweden.

[14/02, 23:20] PHILIP ABRAHAM CBC: Love can be perilous 😄

The tale of Queen Samaris and her love for the Phantom is a cautionary tale 🙃

Thank you for reminding me of this book. Hers is a fascinating story. Loved by two men. One, who become an Emperor and the other, a King. Both children of the Revolution.

Many great historical epics and romances were published in the 1950s. 

A few I recollect, 

The Egyptian

 Agrippa's Daughter

Spartacus

There was one about a Philistine queen 

 

Historical fiction fans, help me here !!!!

Books adapted into movies to watch out for...

https://editorial.rottentomatoes.com/article/classic-books-movie-adaptations/

Classics endure because they’re endlessly adaptable. From Shakespearean tragedies to Austenian social satire, these stories and their complex characters are ripe for a new lens, interpretation, and reimagination with every generation that comes across their pages.

Book-to-screen adaptations have spawned some of the biggest movie franchises (J.R.R. Tolkien’s The Lord of the Rings, Suzanne Collins’ The Hunger Games, and J.K. Rowling’s Harry Potter) and pop culture faves (Lauren Weisberger’s 2003 novel The Devil Wears Prada). But there’s something about classic literature — the corsets, the existential dread, the slow-burn longing across a windswept moor — that never gets old.

So as we officially enter the world of Wuthering Heights (in theaters on February 13, 2026) through director Emerald Fennell’s eyes, it’s time to look back at faithful adaptations and modernized retellings of classic literary stories. Think of this list as Hollywood’s take on your high school syllabus.

Pride & Prejudice (2005)

 87%

Based on Jane Austen’s Pride and Prejudice

Starring: Keira Knightley, Matthew Macfadyen, Brenda Blethyn, Donald Sutherland

Directed By: Joe Wright

To Kill a Mockingbird (1962)

 93%

Adapted from Harper Lee’s To Kill a Mockingbird

Starring: Gregory Peck, Mary Badham, Phillip Alford, John Megna

Directed By: Robert Mulligan

10 Things I Hate About You (1999)

 72%

Modernization of William Shakespeare’s play The Taming of the Shrew

Starring: Heath Ledger, Julia Stiles, Joseph Gordon-Levitt, Larisa Oleynik

Directed By: Gil Junger

Frankenstein (2025)

 85%

Based on Mary Shelley’s Frankenstein

Starring: Oscar Isaac, Jacob Elordi, Mia Goth, Christoph Waltz

Directed By: Guillermo del Toro

Barry Lyndon (1975)

 78%

Based on William M. Thackerey’s The Luck of Barry Lyndon

Starring: Ryan O'Neal, Marisa Berenson, Patrick Magee, Hardy Krüger

Directed By: Stanley Kubrick

Of Mice and Men (1992)

 97%

Adapted from John Steinbeck’s Of Mice and Men

Starring: John Malkovich, Gary Sinise, Ray Walston, Casey Siemaszko

Directed By: Gary Sinise

The Count of Monte Cristo (2024)

 97%

Based on Alexandre Dumas’ The Count of Monte Cristo

Starring: Pierre Niney, Bastien Bouillon, Anaïs Demoustier, Anamaria Vartolomei

Directed By: Alexandre de La Patellière, Matthieu Delaporte

William Shakespeare's Romeo & Juliet (1996)

 74%

Modernized adaptation of William Shakespeare’s play The Tragedy of Romeo and Juliet

Starring: Leonardo DiCaprio, Claire Danes, Brian Dennehy, John Leguizamo

Directed By: Baz Luhrmann

Emma (1996)

 84%

Based on Jane Austen’s Emma

Starring: Gwyneth Paltrow, Jeremy Northam, Toni Collette, Greta Scacchi

Directed By: Douglas McGrath

A Tale of Two Cities (1935)

 93%

Adapted from Charles Dickens’ A Tale of Two Cities

Starring: Ronald Colman, Elizabeth Allan, Edna May Oliver, Blanche Yurka

Directed By: Jack Conway

The Hunchback of Notre Dame (1996)

 80%

Loosely based on Victor Hugo’s The Hunchback of Notre-Dame

Starring: Tom Hulce, Demi Moore, Tony Jay, Kevin Kline

Directed By: Gary Trousdale, Kirk Wise

Anna Karenina (2012)

 63%

Adapted from Leo Tolstoy’s Anna Karenina

Starring: Keira Knightley, Aaron Taylor-Johnson, Jude Law, Domhnall Gleeson

Directed By: Joe Wright

The Three Musketeers (1993)

 33%

Loosely based on Alexandre Dumas’ The Three Musketeers

Starring: Charlie Sheen, Kiefer Sutherland, Chris O'Donnell, Oliver Platt

Directed By: Stephen Herek

Jane Eyre (2011)

 85%

Based on Charlotte Brontë’s Jane Eyre

Starring: Mia Wasikowska, Michael Fassbender, Jamie Bell, Judi Dench

Directed By: Cary Joji Fukunaga

The Great Gatsby (2013)

 49%

Based on F. Scott Fitzgerald’s The Great Gatsby

Starring: Leonardo DiCaprio, Tobey Maguire, Carey Mulligan, Joel Edgerton

Directed By: Baz Luhrmann

Dr. Jekyll and Mr. Hyde (1931)

 91%

Adapted from Robert Louis Stevenson’s Strange Case of Dr. Jekyll and Mr. Hyde

Starring: Fredric March, Miriam Hopkins, Rose Hobart, Holmes Herbert

Directed By: Rouben Mamoulian

The Man in the Iron Mask (1998)

 32%

Inspired by Alexandre Dumas’ The Vicomte de Bragelonne

Starring: Leonardo DiCaprio, Jeremy Irons, John Malkovich, Gérard Depardieu

Directed By: Randall Wallace

Crimes and Misdemeanors (1989)

 92%

Modern interpretation of Fyodor Dostoevsky’s Crime and Punishment

Starring: Martin Landau, Woody Allen, Mia Farrow, Alan Alda

Directed By: Woody Allen

The Green Knight (2021)

 89%

Based on the poem Sir Gawain and the Green Knight

Starring: Dev Patel, Alicia Vikander, Joel Edgerton, Sarita Choudhury

Directed By: David Lowery

War of the Worlds (2005)

 76%

Based on H. G. Wells’ The War of the Worlds

Starring: Tom Cruise, Dakota Fanning, Miranda Otto, Justin Chatwin

Directed By: Steven Spielberg

Effects of AI ~ Deepak Kumar

 

Something Big Is Happening — But We've Been Here Before

Every few years, the world convinces itself that this time is different.

“This is unprecedented.”

“This changes everything.”

“There’s no coming back from this.”

We heard it in 2020.

Remember the early days of COVID?

•No vaccine.

•Experts saying it would take 3–4 years to develop one.

•Hospitals overwhelmed.

•Daily death counts on every screen.

•Borders closed.

•Entire economies frozen.

The question wasn’t political. It was existential.

How will anyone survive this?

But Here’s What We Forget

Humans are terrible at predicting adaptation. We assume that today’s limitation is permanent.

In early 2020, businesses were “doomed,” education systems were “broken,” and travel was “finished.”

Yet within months, vaccines were developed at record speed. Remote work scaled globally. Industries reinvented themselves.

Not perfectly. Not without loss. But undeniably — rapidly.

Crisis Always Feels Permanent

World Wars felt civilization-ending. The 2008 financial crisis felt like economic collapse. The Cold War felt like nuclear extinction was inevitable.

Each time: fear peaked, predictions escalated, and adaptation quietly began.

Fear Is Loud. Adaptation Is Quiet.

Fear spreads faster than solutions. Headlines amplify worst-case scenarios. Uncertainty fuels imagination.

But innovation works silently. Resilience builds gradually. Systems adjust behind the scenes.

By the time we realize it — we’re already adapting.

This Doesn’t Minimize Suffering

COVID was real. Loss was real. Economic damage was real.

Acknowledging resilience is not denying pain. It’s recognizing that humanity is stronger than its worst week.

The Real Lesson

In the darkest months, people said: “There’s no vaccine.” “This will take years.” “Life as we know it is over.”

Yet within two years, vaccines existed. Travel resumed. Schools reopened. Businesses rebuilt.

Not identical. But functional. Alive. Moving forward.

Final Thought

Every generation believes it is witnessing the most fragile moment in history.

Every generation also underestimates its own capacity to adapt.

Something big may be happening. But so is human resilience.

Written by Deepak Kumar

Generative AI and Regression Model

 1. Why Gen AI Sometimes Gives Silly Maths Answers

A helpful way to explain this (building on regression‑model intuition):

Generative AI isn’t a calculator — it’s a probability engine.

Large Language Models (LLMs) don’t compute numbers the way spreadsheets, finance systems, or calculators do.

Instead:

They predict the next most likely word/token based on patterns in their training data.

Maths answers require deterministic logic, but LLMs operate on linguistic probability.

So if the training patterns around certain numbers are messy or sparse, the model may output:

a plausible‑sounding guess

rather than a correct calculation

That’s where the phrase:

“Generative AI lives in a world of words and not numbers”

comes from — because the model’s “native language” is text patterns, not symbolic reasoning.

A simple analogy you can use:

“If you ask a novelist to produce a balance sheet from memory without a calculator, they might write something plausible but still wrong. That’s what LLMs do — they write what looks right, not what is mathematically validated.”

2. How to Clarify the Concept for Finance Colleagues

Since finance people default to logic, controls, precision and reconciliation, explain it this way:

LLMs don’t run formulas; they autocomplete.

Accuracy is not guaranteed unless the model is connected to a tool that performs real calculations (e.g., Python, Excel, a finance engine).

When LLMs hallucinate, it’s because they “sound right” rather than “compute right.”

A phrasing that resonates with finance audiences:

“Think of Gen AI as an expert in predicting language, not executing algorithms. Without a computational back‑end, its maths is like an estimate written in prose rather than a validated entry in Workday or Adaptive.”

3. About the Image You Shared (“8 forces reshaping finance by 2030”)

You noted the text was unclear — and you’re absolutely right:

The resolution is too low to read the items clearly.

If you want, upload a higher‑resolution version or a cropped close‑up, and I can extract the text cleanly for you.

Your explanation of Gen AI as a kind of large‑scale regression engine really clicked — it makes sense why it sometimes gives odd answers to simple maths. The phrase “Generative AI lives in a world of words and not numbers” is thought‑provoking, and here’s how I’ve come to interpret it:

LLMs don’t truly calculate — they predict the next likely word based on patterns in text. So when it comes to maths, they generate something that sounds mathematically reasonable rather than something computed. It’s language prediction, not numerical logic, which explains the occasional silly answers.

at a mathematical level, many AI systems—especially neural networks—do behave like extremely large, stacked, non‑linear regression models. This framing helps explain why they sometimes behave unpredictably.

Below is a simple breakdown.

🔹 1. Regression = Predicting an Output from an Input

Regression models learn a relationship between inputs (X) and outputs (Y) by fitting parameters that minimize error.

This aligns with definitions surfaced in the enterprise search results:

Regression predicts continuous outputs by learning relationships between variables. [geeksforgeeks.org]

Machine learning regressions model Y = f(X). [builtin.com]

Regression techniques underpin forecasting, risk scoring, and trend estimation. [geeksforgeeks.org]

This basic principle extends to AI models.

🔹 2. Neural Networks = Millions of Regression Layers Stacked Together

Each neuron in a neural network performs something very similar to linear regression:

output = (weight1 × input1) + (weight2 × input2) + … + bias

Then the result passes through a non‑linear activation function, allowing the network to model extremely complex relationships—not just straight lines.

So:

One neuron ≈ one tiny regression.

One layer ≈ many regressions combined.

A deep model ≈ many layers of regressions chained together.

This matches enterprise guidance that deep learning builds on patterns and probability distributions across data, not direct logic rules. [AI & Machi...arning POV | PowerPoint]

🔹 3. Training = Fitting Millions or Billions of Parameters

Just like linear regression minimizes error (e.g., least‑squares), AI models:

Compare predicted vs. actual output.

Measure error via a “loss function”.

Adjust parameters using gradient descent.

This echoes what regression tutorials emphasize—minimizing prediction error and improving the best‑fit function. [bigdataelearning.com]

But AI models do this at a much larger scale, often adjusting billions of parameters instead of a handful of regression coefficients.

🔹 4. Generative AI = Predicting the Next Output Using a Learned Probability Function

Here’s where the regression analogy becomes very powerful:

LLMs like GPT or Claude don’t “think”—they predict the next token (word, symbol) using a huge mathematical function learned from data.

That function behaves like a gigantic non‑linear regression:

Next Word = f(all previous words)

Where f is a massive neural network that estimates probabilities.

Enterprise context also notes that generative AI works by understanding probability distributions of language rather than executing deterministic rules. 

This is why you said:

“Gen AI lives in a world of words, not numbers.”

Exactly—because it’s predicting linguistic probability, not performing symbolic computation.

🔹 5. So Why Does It Sometimes Get Maths Wrong?

Because:

AI predicts what looks statistically likely, not what is computationally correct.

Maths requires discrete, exact logic.

Language models only approximate patterns from text data.

This echoes the caution in EU AI guidance: traditional regression and optimization systems are not considered AI systems, because AI models behave probabilistically, not deterministically. [EU AI Act FAQs | dentsu Dot]

So in a sense:

Calculators = logic

AI = probability

And that explains the “silly answers”.

🔹 6. One‑Sentence Summary You Can Give:

Here’s a tight version you can send back:

“Modern AI systems are essentially massive stacked regression models that learn a complex probability function over text. They don’t ‘calculate’ like a finance engine—they predict the most likely next token, which is why they sometimes produce wrong maths but fluent language.”

And yes — the diagram on “8 forces reshaping finance by 2030” is interesting.

1. The end of transactional customisation
2. Rise of Do it yourself tech
3. Machine dominated decision making
4. A workforce of AI agents
5. The era of discontinuous regulatory changes
6. The lonely enterprise
7. The finance talent crash
8. Maximally matrixed organisation