Microsoft 50

 

Today, Gates says that the 50th anniversary of Microsoft is “bittersweet,” and that it feels like yesterday when he and Allen “hunched over the PDP-10 in Harvard’s computer lab, writing the code that would become the first product of our new company.” That code, he says, remains “the coolest code I’ve ever written to this day …. I still get a kick out of seeing it, even all these years later.”

Bill Gates writes on his Gates Notes website. “In 1975, Paul Allen and I created Microsoft because we believed in our vision of a computer on every desk and in every home. Five decades later, Microsoft continues to innovate new ways to make life easier and work more productive. Making it 50 years is a huge accomplishment, and we couldn’t have done it without incredible leaders like Steve Ballmer and Satya Nadella, along with the many people who have worked at Microsoft over the years.”

Google Wiz

 

Today, Google has announced its largest acquistion to date, agreeing to purchase cybersecurity firm Wiz for a staggering $32 billion in an all-cash deal.

Wiz, a rapidly growing startup founded in Israel, has quickly become a majoy player in the cloud security speace.  It's AI driven solutions have attracted an ipressive clientele including 50% of Fortune 100 companies.

The timing of this deal is particularly noteworthy.  It comes after a previous attempt by Google to acquire Wize for $23 billion fell through in 2024 due to antitrust concerns.

This acquisition bolsters Google Cloud's security capabilities, positioning it more competitively against industry giants.

Transformational Education

This month, blessed to login Bishop Heber campus as chief guest after 3 decades being post grad student.

Beyond the nostalgic memorable rewinds, wanted to ink about the importance of continuous education.

During the senior leadership team of college board (prior to students session), got two personal questions beyond other topics.

1. What's the materialistic motivation in industry to earn Doctorate?
2. Is MBA required for industry leader role?

In my lens, both are surprising, provoking and powerful queries from industry to institute.

My answers were
1. Zero benefits on hike, promotion, etc; but personally complete transformation on deep learning of the computers between 1992 masters and 2022 PhD
2. Original (silly) drive was to become the alumini of prestigious legacy red bricks CEG, Madras, which got disqualified after my high school. During MBA classes, learnt few formal methods/techniques to shape myself as true leader in the industry.

In tum, both Q&A emphasize my favorite motivational quote - "Education is only way to transform poor in the society".

Microsoft Quantum

A couple reflections on the quantum computing breakthrough we just announced...

Most of us grew up learning there are three main types of matter that matter: solid, liquid, and gas. Today, that changed.

After a nearly 20-year pursuit, we’ve created an entirely new state of matter, unlocked by a new class of materials, topoconductors, that enable a fundamental leap in computing.

It powers Majorana 1, the first quantum processing unit built on a topological core.

We believe this breakthrough will allow us to create a truly meaningful quantum computer not in decades, as some have predicted, but in years.

The qubits created with topoconductors are faster, more reliable, and smaller.

They are 1/100th of a millimeter, meaning we now have a clear path to a million-qubit processor.

Imagine a chip that can fit in the palm of your hand yet is capable of solving problems that even all the computers on Earth today combined could not!

Sometimes researchers have to work on things for decades to make progress possible.

It takes patience and persistence to have big impact in the world.

And I am glad we get the opportunity to do just that at Microsoft.

This is our focus: When productivity rises, economies grow faster, benefiting every sector and every corner of the globe.

It’s not about hyping tech; it’s about building technology that truly serves the world.

Token embedding vector

Token embedding vectors are a way to represent words or sub words as numerical vectors in a high-dimensional space. 

These vectors capture the semantic meaning of the tokens, allowing AI models to understand the relationships between words and their context within a sentence or document.

1. Tokenization: The input text is first broken down into individual units called tokens. These can be words, sub words, or even characters, depending on the specific model and task.
   
   
2. Embedding: Each token is then mapped to a corresponding vector in a high-dimensional space. This mapping is learned by the model during training, where it analyzes vast amounts of text data to understand the relationships between words.
   
   
3. Vector Representation: The resulting vectors are dense and continuous, meaning that each element in the vector is a real number. The position of a token in this vector space reflects its semantic meaning, with similar words having vectors that are closer together.

As a simple given example, the embeddings for our tokens consist of vectors with three elements.

Text Analytics

Text Analytics is Microsoft AI library with the below tasks

• Tokenization: Breaking text into individual words or phrases (tokens).
• Stop Word Removal: Removing common words (e.g., "the," "a," "is") that don't carry much meaning.
• Stemming/Lemmatization: Reducing words to their root form (e.g., "running" to "run"). Stemming is a simpler, rule-based approach, while lemmatization uses dictionaries and is more accurate.
• Part-of-Speech Tagging: Identifying the grammatical role of each word (e.g., noun, verb, adjective).
• Named Entity Recognition (NER): Identifying named entities like people, organizations, and locations.
• Sentiment Analysis: Determining the emotional tone of text (positive, negative, neutral).
• Topic Modeling: Discovering underlying topics in a collection of documents.
• Text Classification: Assigning categories or labels to text.

Insane Journey of DeepSeek

The Insane Story of DeepSeek’s Founder, Liang Wenfeng 

DeepSeek didn’t just build an AI—Liang Wenfeng turned constraints into competitive advantages. 

Here’s how setbacks fueled game-changing innovations.

Implications:

• DeepSeek’s efficiency-first AI models are shifting the competitive landscape.
• Compute scarcity forces companies to rethink AI scaling strategies.
• China’s AI labs are proving that cost-effective models can challenge U.S. AI dominance.
• Nvidia’s stock volatility underscores market sensitivity to compute-efficient AI models.

Looking Ahead

• AI innovation is no longer about who spends the most—it’s about who does more with less.
• Investors and businesses must adapt to a world where efficiency is king.
• The AI race is evolving fast—staying informed isn’t optional.