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

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

India’s New Labour Codes and Their Impact on Gratuity for Employees

India’s labour landscape has undergone one of the most transformational reforms in its post‑independence history. With the enforcement of the four new Labour Codes—the Code on Wages (2019), the Industrial Relations Code (2020), the Occupational Safety, Health and Working Conditions Code (2020), and most critically, the Code on Social Security (2020)—beginning 21 November 2025, the country has shifted from a fragmented set of 29 central labour laws to a modern, unified regulatory framework. 

These reforms were driven by a long‑standing need to replace India’s outdated colonial‑era labour legislation with a system that matches contemporary workplace realities. Laws governing wages, social security, dispute resolution, and safety were previously spread across numerous statutes, leading to confusion, uneven enforcement, and limited protections for the country’s large informal and contract‑based workforce. The new Codes promise simplification, uniformity, and inclusiveness: clearer definitions, predictable compliance standards, and social security coverage that expands beyond the traditional formal workforce. 

Among these reforms, the Code on Social Security, 2020 stands as a cornerstone because of its effort to bring every category of worker—organised, unorganised, fixed‑term, gig, and platform—under one cohesive umbrella. It consolidates nine earlier laws including the Employees’ Provident Fund Act, Employees' State Insurance Act, Maternity Benefit Act, and importantly, the Payment of Gratuity Act, 1972, creating a unified, modern framework for social protection. The Code emphasises universal access to benefits such as provident fund, health insurance, maternity care, and gratuity, and introduces dedicated social security boards for gig and unorganised workers, marking a strong shift toward inclusive labour welfare.

A particularly significant area affected by these reforms is gratuity, a statutory terminal benefit traditionally associated with long‑term service. Under the earlier regime, gratuity was payable only after five years of continuous service (except in cases of death or disablement). This created a large protection gap for workers on fixed‑term contracts or in industries with shorter employment cycles. The new Labour Codes, however, modernise and broaden gratuity entitlements in ways that fundamentally reshape employee benefits.

Under the new gratuity provisions, the five‑year service rule continues for permanent employees, but fixed‑term employees now qualify for gratuity after just one year of continuous service, with payment on a pro‑rata basis. This is a transformative shift that expands coverage to sectors like IT, BPO, manufacturing, and gig‑based platforms where project‑based and short‑term contracts are the norm. 

Equally impactful is the new standardised wage definition, which mandates that at least 50% of an employee’s total remuneration must count as “wages” when calculating statutory benefits—including gratuity. If allowances exceed 50%, the excess is added back to wages for the purpose of calculation. This change elevates the base on which gratuity is computed, often increasing the final payout for employees whose employers previously kept basic pay low and allowances high. 

Thus, the new gratuity regime has a two‑fold effect on employees. First, more employees qualify, especially those in fixed‑term or contract roles who were historically excluded. Second, the amount they receive is likely to be higher, due to the 50% wage rule that lifts the calculation base. Together, these changes reflect the government’s intention to recognise modern employment dynamics and provide fairer, more inclusive financial security to India’s diverse workforce.

In essence, the new Labour Codes—particularly the Code on Social Security, 2020—mark a decisive shift in India’s social protection philosophy. By simplifying the law, expanding eligibility, and strengthening the structure of benefits like gratuity, the Codes not only support workers’ long‑term welfare but also encourage greater formalisation and transparency across industries. As India’s workforce continues to evolve, these reforms lay the foundation for a more secure, equitable, and future‑ready labour ecosystem.

The gratuity formula remains:

Gratuity = (Last drawn wages × 15 × Years of service) / 26

But now “wages” means at least 50% of total CTC, leading to higher payouts.

Under the new Codes, wages must constitute at least 50% of total remuneration for gratuity calculations:

Includes Basic Pay + Dearness Allowance + Retaining Allowance

If allowances exceed 50%, the excess is added back to wages for statutory calculations

Kate Chopin Short Stories

 

Beyond the Bayou (1893) follows La Folle, a large, solitary Black woman living on the Bellissime plantation in Louisiana. Traumatized as a child after witnessing P’tit Maître—then a young man—stumble into her mother’s cabin covered in blood and pursued by enemies, she developed a profound psychological fear. Since that day, she refuses to cross an imaginary boundary beyond the bayou that curves around her cabin. 

La Folle lives simply and independently, tending her land, and knowing almost nothing of the world beyond her self‑imposed boundary. Though isolated, she maintains a tender bond with Cheri, the ten‑year‑old son of P’tit Maître, whom she loves more deeply than the other plantation children. He visits her often, bringing food, companionship, and comfort. 

One afternoon, Cheri wanders into the woods with his new rifle. La Folle later hears a gunshot followed by his cry of pain. She rushes toward the sound and finds Cheri wounded, terrified he might die. She lifts him into her arms and carries him toward the plantation house—but at the edge of the bayou, her trauma grips her, and she freezes. Finding no help nearby, she summons her courage, pushes through her fear, and crosses the bayou for the first time since childhood to save the boy. 

Her crossing becomes a moment of communal astonishment—neighbors shout the news as she races through the plantation. Collapsing at P’tit Maître’s doorstep after delivering Cheri safely, she later awakens in her cabin under the care of Tante Lizette. Though physically weak, La Folle is transformed: the act of crossing the bayou breaks the psychological barrier that had confined her for decades.

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Ma’ame Pélagie centers on a proud, aging Creole woman named Pélagie, who lives with her younger sister Pauline near the ruins of their once-grand family mansion on Côte Joyeuse in Louisiana. The mansion, built by their father Philippe Valmet, was destroyed during the Civil War—only the thick walls, pillars, and bits of stone flooring remain, now overgrown with vines. Pélagie, now fifty, lives in a modest three‑room cabin in the shadow of these ruins. Her unwavering dream is to rebuild the ancestral home, and she and Pauline have spent thirty years saving every penny (“picayunes hoarded”) for this purpose, though still far from having enough. 

Pélagie’s entire life revolves around preserving the memory of the old house and the family’s former grandeur. Pauline, gentle and compliant, follows Pélagie’s lead but barely remembers the splendors of their past—she was a child during the war, rescued by Pélagie from fire and chaos after a revolt and destruction swept the estate. Their brother, Léandre, who knew more of the past, left long ago to live in cities. 

The sisters spend their days in quiet routine, sitting on the ruined portico, drinking black coffee, reminiscing about the old times, and imagining the restoration of their home. Despite decades of sacrifice, the grandeur they dream of—marble pillars, crystal chandeliers—may never be fully restored, but Pélagie clings to this vision with fierce devotion. 

While other versions of the story include extended chapters involving Léandre’s daughter returning from abroad and challenging Pélagie’s attachment to the past, the core narrative remains a poignant portrayal of memory, loss, and a life spent mourning what cannot be recovered. Chopin depicts a woman whose identity is so entwined with her past that she struggles to move forward, making the ruins not just a physical space but a symbol of emotional confinement. 

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Set in antebellum Louisiana, Désirée’s Baby begins with Madame Valmondé traveling to the L’Abri plantation to visit her adopted daughter Désirée and her newborn baby. Désirée had been found as a toddler, abandoned at the Valmondé estate gate, with no known parentage. She grew up gentle and beautiful, and later married Armand Aubigny, a wealthy plantation owner who fell instantly and passionately in love with her, dismissing concerns about her mysterious origins. 

At first, the marriage is joyful. The birth of their son softens Armand’s harsh temperament—he becomes more tender toward Désirée and temporarily less cruel to the enslaved people he oversees. But when the baby is about three months old, Désirée notices a troubling change: neighbors whisper, servants stare, and Armand grows cold and distant. One day, observing her child next to La Blanche’s mixed‑race son, Désirée realizes with horror that her baby shows signs of African ancestry.

When Désirée confronts Armand, he cruelly declares that the child is not white—and therefore, neither is she. Distraught, Désirée writes to her mother seeking confirmation of her lineage, but Madame Valmondé only urges her to come home. Armand rejects Désirée, claiming she has brought disgrace upon his name. Heartbroken, Désirée takes her baby into the bayou and disappears, never to return. 

In a devastating twist, Armand later burns all of Désirée’s belongings in an attempt to erase her from his life. While doing so, he finds an old letter from his own mother, revealing that she belonged to “the race that is cursed with the brand of slavery.” Armand—not Désirée—was the one with mixed ancestry all along.

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A Respectable Woman

The story takes place on a Louisiana sugar plantation, where Mrs. Baroda learns—much to her annoyance—that her husband, Gaston Baroda, has invited his old college friend Gouvernail to stay for a week or two. She had been looking forward to a quiet period alone with her husband after a busy social winter. 

Although she has never met Gouvernail, Mrs. Baroda has formed an unfavorable mental image of him: tall, cynical, glasses-wearing, and aloof. When he arrives, however, he turns out to be neither cynical nor arrogant—just quiet, polite, and content to relax on the porch, listening to Gaston and enjoying the peaceful plantation. To her surprise, Mrs. Baroda begins to like him, though she cannot explain why. 

Her unexpected attraction leaves her unsettled. She grows restless and contemplates leaving the plantation temporarily. One evening, Gouvernail joins her on a bench, unaware of her inner conflict. For the first time, he speaks freely about his life and longing for simplicity. Mrs. Baroda listens more to the tone of his voice than the words and feels a powerful impulse to move closer to him—but she resists, reminding herself that she is “a respectable woman.” 

The next morning, she departs to stay with relatives until after Gouvernail leaves. When Gaston later suggests inviting his friend again next summer, she initially refuses but eventually changes her mind. She tells her husband she has “overcome everything”, though Chopin leaves her intentions deliberately ambiguous—leaving readers to wonder whether Mrs. Baroda has overcome her desire or merely her hesitation. 

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The Kiss — Summary (Kate Chopin, 1895)

The Kiss is a short story about Nathalie, an ambitious young woman who wants both passion and wealth—but ultimately must choose between them.

Nathalie has been cultivating a relationship with Brantain, a shy but extremely wealthy man whom she intends to marry for financial security. While Brantain visits her one evening, her secret lover, Harvy, enters the room unexpectedly and kisses her, unaware that Brantain is present. The kiss jeopardizes her carefully laid plans.

Nathalie quickly invents a story to downplay the incident, insisting Harvy is just an old family friend. Though shaken, Brantain eventually accepts her explanation. Later, at a social reception, Nathalie approaches Brantain, and he forgives her—leading to their marriage.

However, after the wedding, Harvy congratulates her and adds with a hint of cruelty that he will never again kiss a married woman. Nathalie realizes then that her choice of money over love is final—she has gained wealth but lost genuine passion.

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A Pair of Silk Stockings

Summary:

This story follows Mrs. Sommers, a weary mother who unexpectedly finds herself with a small sum of money. Initially planning to spend it on her children, she is overcome by a powerful, almost instinctive desire for self‑indulgence—a feeling she has long suppressed due to poverty and domestic demands. She buys herself a luxurious pair of silk stockings, which triggers a series of impulsive purchases: new shoes, gloves, a magazine, and a nice lunch.

For one afternoon, she experiences comfort, dignity, and a sense of self she rarely accesses. The story ends with her reluctant wish that the day would never end—a quiet acknowledgment of how fleeting her personal pleasures are in a life defined by sacrifice.

Themes:

Self‑identity vs. maternal duty

The intoxicating pull of luxury

Class, poverty, escapism

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The Locket

Summary:

This Civil War–era story centers on Edmond, a young Confederate soldier, and his beloved Octavie, who gives him a locket containing her portrait as a symbol of devotion. When Edmond is reported dead after a battle—and the locket found on another soldier—Octavie is devastated.

Later, the truth emerges: Edmond is alive—the locket had been stolen from him before the battle. The two are reunited, but the story leaves an emotional note about the uncertainties of war and the fragile nature of human connections.

Themes:

Love and separation

The randomness and cruelty of war

Symbolism of keepsakes and identity

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A Reflection

Summary:

One of Chopin’s briefest and most philosophical pieces, A Reflection presents a woman contemplating life’s contradictions. She observes that people often struggle to achieve happiness because the world is full of illusions, suffering, and fleeting joys.

The key idea is that human beings rarely appreciate happiness until it is gone, and that much of life is defined by disappointment or the fading of pleasure. The tone is contemplative and slightly pessimistic, echoing Chopin’s interest in emotional clarity and social constraints.

Themes:

The nature of happiness

Human dissatisfaction

The gap between expectation and reality

Synchronicity & Anthony Hopkins

 

As received, thought need to save this future reference. 

Anthony Hopkins couldn't find a book anywhere in London. Then he sat down on a subway bench.

It was 1973. Hopkins had just landed a role in a film called "The Girl from Petrovka," based on a novel by American journalist George Feifer.

Like any serious actor, he wanted to read the source material. He spent an entire day searching bookshops along London's famous Charing Cross Road.

Nothing. The book wasn't available anywhere in the UK.

Frustrated and defeated, Hopkins walked into the Leicester Square Underground station to catch a train home.

That's when he noticed something on a bench.

Someone had left a book behind.

He picked it up. Turned it over.

"The Girl from Petrovka."

The exact book he'd been searching for all day, abandoned on a subway bench in a city of eight million people.

Hopkins couldn't believe it. He took it home, read it, and noticed something strange. The margins were filled with handwritten notes in red ink. Annotations. Someone had marked up this copy extensively.

He didn't think much of it. He used the notes to help him understand his character, prepared for his role, and filed the coincidence away as one of life's unexplainable moments.

Months later, Hopkins traveled to Vienna where the film was being shot.

One day on set, he was introduced to a visitor.

George Feifer. The author of the book.

They talked about the film, the characters, the story. Then Feifer mentioned something that made Hopkins stop cold.

"I don't have a copy of my own book anymore," Feifer said. "I lent my personal copy to a friend a couple of years ago. It had all my notes in the margins. He lost it somewhere in London. I've never seen it since."

Hopkins felt the hairs on the back of his neck stand up.

"I found a copy," he said slowly. "On a bench in the Underground. It has handwritten notes throughout."

Feifer looked at him skeptically.

Hopkins retrieved the book from his things and handed it to the author.

Feifer went pale.

It was his copy. His handwriting. His annotations. The personal copy he'd lent to a friend years earlier, which had somehow ended up abandoned on a subway bench at the exact moment Anthony Hopkins, the actor who needed it most, happened to sit down beside it.

In a city of millions. Across thousands of streets. Among hundreds of tube stations.

The right book. The right bench. The right moment.

George Feifer got his lost book back. Anthony Hopkins got a story he would tell for the rest of his life.

Carl Jung called it synchronicity, the idea that meaningful coincidences aren't random but reflect some deeper pattern in the fabric of reality.

Hopkins has always been fascinated by the concept. He's spoken in interviews about learning to simply be amazed by life.

"I don't know if there's a master plan," he once said. "But sometimes things happen that are just too perfect to explain."

Maybe it was luck. Maybe it was fate. Maybe it was just the universe having a bit of fun.

Or maybe, just maybe, some books are meant to find their readers.

And some stories are meant to be told.

*The legendary actor! 😄 Anthony Hopkins is a British icon known for his incredible performances in films like "The Silence of the Lambs" (Hannibal Lecter), "Gladiator", and "Thor". 

Synchronicity - what an amazing concept.