Thoughts on Project Management

Today, during a session on understanding a project, I shared this format which the class loved. 

To understand all the dimensions of a project, just go over the 7 vibhaktis of Sanskrit 

Karta ne - कर्ता  ने - Who is doing this project? 

Karm Ko - कर्म को - Exactly WHAT do we want done? 

Karn se - करण से/के द्वारा - What are the tools/tech platforms that we will use to deliver this project? 

Sampradan ke liye - संप्रदान के लिए  - For whom is the project being done? (End users and key stakeholders) 

Apadaan se prithak - अपादान से पृथक -   Who are the people/entities who will lose some ground/role after this project goes live? Who will be alienated? 

Sambandh Ka, Ke, Ki - संबंध का, के, की - All related entities that share an interface/data exchange with the project. 

Adhikaran Mein/par - अधिकरण में/पर - What all will this project encompass? What business processes does it own? 

We created a 360-degree view of the project using this thought process and it was a huge success, allowing us to identify our stakeholders and our scope in a fun way. 

Help me create my next story

 #MondayMorning

Let's do something fun. Work on this plot with me.

The time is Feb 2026. Gen AI is now writing about 40% of all production deployed code. The debugging and code logic check is being done by another Gen AI engine. Human coders are not involved.

A government entity, in a bid to save costs, uses the same model to write code for a government website.

The website automatically matches each citizen to all the welfare schemes that are applicable to them.

The citizen has to enter their family income, location, type of housing, family size, composition (senior citizens, children, etc.) and the system automatically matches them to the welfare schemes applicable to them in that state of residence (free health insurance, meal coupons, priority nutrition consultation, disability pension, etc.)

After 6 months, post a routine update to the firewall, the system administrator notices a data leak alert.

Upon investigation, it is found that there is a simple, one line injection that sends a copy of all citizen data to the creators of that Gen AI (similar to dialing home in browsers).

When the Gen AI company is summoned by the government, it argues that since the code was generated by an autonomous installation being used by government employees, they could not possibly have had any knowledge of this injection, nor have they, at any time, accessed the location (cloud storage) where this data is purportedly being sent. This is found to be true.

Through a detailed forensic analysis, it is uncovered that the LLM engine deliberately created this storage location on the cloud servers of the parent company and then stored this data. All pull and post requests to this server (data storage and retrieval) is being done by the resident LLM engine on govt servers only. 

Now, the investigators are puzzled. The trick is really simple - create a tiny but powerful injection in the code. The code used standard malware propagation techniques to avoid detection. But the question is - WHY did the LLM do this? 

So, in your view, WHY was this injection was created by the LLM? What are the possible ways in which this data can be used by an LLM?

Dumb and Dumber

Last week, the most important paper being discussed in AI was the Apple Research - telling us that AI is not as good at deductive logic as the industry would like us to believe. It is still, to put it mildly, rather dumb. 

This week, the hottest paper is the MIT research telling us that adults who use Gen AI are losing cognitive skills. Gen AI is making humans dumber. 

To sum up, AI is dumb, and humans are getting dumber. 

So, this fortnight of research is hereby summed up as: Dumb and Dumber. 

When ChatGPT tells you your MBTI type

Over the weekend, Saloni and I got playing with ChatGPT. She was exploring its use in therapy and I just decided to explore personality types. 

Our approach was to give it pieces of our writing and asking it to analyse that. 

I first asked about the Jungian personality archetype that I am most likely to fall into. The explanation given by ChatGPT seemed logical enough. 

And then, I asked it to guess my MBTI type based on the content given. 

Now, this is a game I have played often with CoPilot. So I was not looking for miracles. 

BUT, to my surprise, within no time at all, it gave me the accurate MBTI type. 

Not easily convinced, I asked it to also give me the difference between my T and F dimensions. It was accurate on that too!! 

Go on, try it! 

Obituary for Jasjeet sir

When he was trying to explain something to you and you were generally unable to comprehend (read: being a blockhead), he would not show even a sliver of irritation. He would address you as "Raje" and explain again. S-l-o-w-e-r this time. But he would explain until you did it right. Quality of output was not negotiable. 

Whether it was over chai or in office meetings, the smile was ever-present and hugely infectious. It is impossible to forget the cheer that always accompanied his presence. 

On social media, he made posts for his parents, and for his children. For ma'm, his love was easy to see. They recently finished 36 years of togetherness. 

So, no, it was not an easy message to read at 6 30 am on a Sunday. One stared at the photograph in the message for a long time. I don't think I have ever seen him without that easy smile, even in the middle of the toughest conversations. It was still there. Making the message even harder to believe. 

He was an easy mentor to have. Easy, because he made mentoring look easy. When you asked him for advice, he was direct, blunt, and super honest. If you asked him what you needed to do, he told you. In Punjabi, we have a word - Lag-labed. Roughly, it translates to circumlocution with an intent to clutter or confuse. He never had that. 

He carried himself lightly. 

At GDC and then at Labs, he was an active member of the social contribution team. Even though he was a senior leader, he made time for this voluntary group. To him, it just was the most natural thing to do.

In kindness and empathy, he was like a father figure. In conversation, like a friend.  

It is now 3:00 am the following morning. And I am typing out this obituary. He was a mentor, teacher, senior. A nurturer. A Smiler. And so much more.  

Jasjeet sir, you will be much missed.  

Yesterday, some of us from SAP reconnected. Unable to understand this. Sharing a grief. As if the square white table of the cafeteria was back, and we were sitting around it, chai in hand. But one chair was empty. 

Words I identify with

 Dendrophile 

What China did right in preparing for the age of AI

 Within weeks, the market is flooded with Chinese LLMs - either free to use, or significantly cheaper than known LLMs.

They are flooding the market faster than countries can ban them. Deepseek, in a predictable move, has upped its prices.

I am wondering:
A. HOW are so many LLMs ready for commercial release within such a short time?
B. WHY are so many LLMs flooding the market?

Any thoughts?

Update:
So, I did some digging and found out some very important things.
1. China has as many as 130+ approved LLMs. Unlike the US, in China, every LLM has to be approved by the government before it is available for public use.
2. 600 million people use these LLMs.
3. Chinese companies support each other. They embed each other's solutions.
4. China unveiled an AI policy in 2017. It invested in policy making, chip manufacturing, and skill building, consistently from then. All 3 were being done.