Influential Tech Leaders

In 1972, a woman in Cambridge, England, figured out how to make computers understand what we’re actually looking for. Her name was Karen Spärck Jones. https://lnkd.in/g8tdZ-cQ At the time, searching through documents meant reading titles, checking indexes, or hoping you remembered the right keywords. It was slow, manual work. Karen was working with punch cards and early computers, and she realized something simple but powerful: common words like “the,” “and,” or “of” show up everywhere and don’t help you find anything specific. A rare word, on the other hand, is much more useful. She created a mathematical formula that weighed how important a word was in a particular document against how common it was across the entire collection. She called it term frequency-inverse document frequency — TF-IDF. It let a machine figure out relevance without actually understanding the meaning of the words. It was a quiet paper in a niche academic journal. Most people in computing at the time thought language processing was a librarian’s problem, not serious science. Mainframe computers were expensive and mostly used for military calculations, banking, and census data. Karen had to wait for the engineers and physicists to finish their work before she could run her experiments late at night on the university’s big Titan computer. She fed in stacks of punch cards, dealt with jammed readers, and checked everything by hand. She didn’t have a flashy lab or big funding. She just kept working. Decades later, when the internet exploded with billions of pages, search engines hit a wall. Early directories relied on humans manually categorizing everything. It couldn’t scale. Engineers digging through old research found Karen’s 1972 paper. They took her math, scaled it up, and built it into the core of how modern search works. Google, Bing, academic databases, even the search function in your email — they all use some version of what she created. You type a question. The system filters millions of documents in a fraction of a second and gives you what you need. That filtering logic traces straight back to her. Karen stayed at Cambridge. She taught, mentored other women in computing, and kept pushing the field forward until she retired in 2002. She died in 2007. She never got rich. She never became a household name. The giant tech companies that built empires on search rarely mentioned her. But every time you type something into a search bar and actually get a useful answer, you’re using Karen Spärck Jones’s thinking. She didn’t build the internet. She just taught machines how to listen better.

Alan Turing is called the father of computing. But the first computer programmer? That was a woman. Ada Lovelace was born in 1815. She was the daughter of the infamous poet Lord Byron and the wealthy, mathematically gifted Annabella Milbanke. When she turned 17, Ada was introduced to Charles Babbage. A brilliant mathematician and inventor who showed her a prototype of his “difference engine,” a mechanical calculator. What began as a mentorship soon became an intellectual partnership. Then came the analytical engine. Unlike the difference engine, which could only perform fixed equations, Babbage’s new machine had memory (“the store”), a processor (“the mill”), and used punch cards to process data. But Babbage, for all his genius, saw the machine only as a number cruncher. Ada saw more. She began advanced studies under Augustus De Morgan, one of the leading mathematical minds of the era. In 1842, Italian mathematician Luigi Menabrea published a paper summarizing Babbage’s lectures in Turin on the analytical engine. Ada translated it into English, and added her own notes. Her notes were 3x longer than the paper itself. She added 7 footnotes, labeled A through G. In Note A, she became the first to distinguish between numbers and symbols, realizing a machine could process not just math but music, letters, and logic. In Note G, she included the first published computer program: an algorithm to calculate Bernoulli numbers using Babbage’s engine. In that same note, Ada wrote what is now called “Lady Lovelace’s Objection”. An early critique of artificial intelligence. “The analytical engine has no pretensions whatever to originate anything. It can follow analysis, but it has no power of anticipating any relations or truths.” This led to what is now known as the Lovelace Test, proposed in 2001: a computer can only be said to have intelligence when it can create something entirely original, without human input. To this day, no AI has passed the Lovelace Test. And then, just as she was getting started, she got sick. In 1851, she was diagnosed with cancer. She died a year later at age 36. Her work was largely forgotten. Until 1953. That year, Bertram Bowden republished her notes in “Faster Than Thought: A Symposium on Digital Computing Machines”. And Ada was reintroduced to the world as the first computer programmer. In the 1970s, the U.S. Department of Defense named a new programming language after her: ADA. Ada believed programming would shape mathematics itself. She believed coding would teach us new ways to think. And she was right. But… why didn’t she get credit? Because she was a woman. She couldn’t publish under her name. She couldn’t enter libraries. She couldn’t attend university. In short: she was born 100 years too early. 💡 Follow Justine Juillard to read 365 stories of women innovators in 2025.

Most people don’t know her name. And yet, almost every device you touch... your phone, your laptop, your Wi-Fi router, depends on a few hundred lines of code she wrote in 1985. Back then, computer networks had a fatal flaw. Backup paths created loops. Data would enter those loops… and spin forever. Packets multiplied, systems froze, entire networks crashed. It was like sending cars onto a roundabout with no exits. Eventually, everything jammed. The internet of the 1980s could not grow unless someone solved this. Radia Perlman did. Working at DEC in the mid-1980s, she created the Spanning Tree Protocol. This brilliant idea allowed switches to talk, detect loops, disable the dangerous ones, and instantly re-route traffic when a primary path failed. She taught networks how to heal themselves. Those few hundred lines of code became the backbone of the modern internet — running silently in offices, data centers, and across continents. As you read this in 2025, her algorithm is quietly protecting global networks from failure. But Radia Perlman walked into rooms where she was mistaken for an assistant. Her work was overlooked, attributed to others, forgotten in footnotes. When people later called her the “Mother of the Internet,” it was a compliment and an irony. Because great engineering is often invisible. And so was she. But she kept creating anyway. Over the 1990s and 2000s, she earned 100+ patents. She wrote textbooks that shaped generations. She developed new security methods. She was inducted into the Internet Hall of Fame in 2014. All built with the same philosophy: Make systems that survive. Make systems that keep going. Make systems that quietly hold the world together. Today, in her seventies, Radia Perlman is still working. And the protocol she wrote almost 40 years ago still runs beneath our digital lives. The internet was built to withstand failure. So was she. And maybe that’s the lesson that sometimes the people who change the world aren’t loud, or famous, or celebrated. Sometimes they’re just… invisible. But their work holds everything up. #INTERNET #inspiration #motivation #wisdom #computer #computerscience

Gurtej Sandhu, a 58-year-old Sikh scientist of Indian origin, has quietly etched his name among the world’s greatest inventors. With more than 1,380 U.S. patents, he has surpassed even Thomas Edison, the man behind the light bulb. Working at Micron Technology in the U.S., Sandhu’s groundbreaking contributions to memory chip technology—such as DRAM and NAND innovations—have transformed smartphones, laptops, and cloud storage. His pioneering methods, like atomic layer deposition and pitch-doubling, have set new industry standards, enabling companies to pack more data into smaller chips. Honored with the IEEE Andrew S. Grove Award, one of the highest recognitions in electronics, Sandhu’s influence is felt across the tech world. Yet, outside industry circles, few know his name. His story is a powerful reminder that the world’s most impactful innovators often work in quiet brilliance, shaping the technology we rely on every single day.

In 1971, a quiet breakthrough changed how humans communicate. Computer engineer Ray Tomlinson sent the first message between two computers on ARPANET, the early network that would evolve into today’s internet. Before this, messages were limited to a single machine. You could leave a note, but only for someone using the same system. Tomlinson changed that. By modifying a program called SNDMSG, he enabled messages to travel across different computers on the network. This became the first version of email. But the most lasting impact came from a small decision. He chose the “@” symbol to separate the user name from the destination machine. Simple. Clear. Scalable. The format user@host became the standard for email addresses and remains unchanged decades later. Billions of people use it every day without thinking about the decision behind it. This is how foundational systems are built. Not always through complexity, but through clarity. The biggest innovations are not always the most visible ones. Sometimes they are small design choices that solve the right problem in the simplest way possible. Because when a solution becomes universal, it disappears into everyday life. And that is when you know it truly worked.

While Western governments argue over industrial policy, China is quietly building the innovation engine of the clean-energy future. China now files three times more clean tech patents than the rest of the world combined. And it's not slowing down. China is surging towards 300,000 patent applications per year, while the US and EU have stagnated and fallen behind. It's also not just solar and batteries. China leads across the board: EVs, heat pumps and inverters as well as all the power electronics to make it work. China has become the global centre of gravity for clean energy innovation. How did this happen? A few factors stand out: ➡️ Decades of consistent industrial strategy with clear 5 and 10-year targets ➡️ Innovation tightly coupled with manufacturing scale, enabling faster iteration and lower costs ➡️ A fully integrated ecosystem: co-located supply chains, aligned incentives and stable long-term policy signals The result isn't just more patents – it's the rapid commercialisation of new technologies that were barely imaginable a decade ago. Things like: ✅ EVs that can charge in 10 minutes ✅ Solar at US10c/W ✅ UHVDC cables that can carry 12 GW over thousands of kilometres ✅ Battery chemistries evolving at record speed ✅ Fast-response inverters that stabilise grids in milliseconds Patent leadership leads to manufacturing scale, cost reductions, booming exports and global dominance. This chart is an early signal of where clean-energy innovation is heading... #energy #renewables #energytransition

For every Altman or Zuckerberg, there’s an Indian Anandkumar or Reddy quietly building the tech world. But, unfortunately, we don’t know about them. Because when it comes to global tech, only the same names dominate — Sam Altman, Mark Zuckerberg, Elon Musk. Here are a few of these unsung heroes: 👉 Raj Reddy – Created the first-ever speech recognition system 👉 Anima Anandkumar – Driving AI breakthroughs at @NVIDIA 👉 Sabeer Bhatia – Revolutionized email with Hotmail long before Gmail showed up 👉 Arun Netravali – Invented digital video compression—thank him for your video calls 👉 Vinod Khosla – Built Sun Microsystems, laying the foundation for the modern internet These innovators have truly transformed the tech landscape. Yet, their contributions often go unrecognized outside their fields. India has given the world some of its greatest tech pioneers. And, I believe it’s on us to make sure their names echo far and wide. After all, stories that can inspire generations should not die :) Thoughts?

This episode took me 8 years of convincing the guest to make it happen. I first met Jerry Murdock at the Connaught Hotel to discuss a company we were co-investing in. I was 21 and had just raised my first fund. Jerry, as the founder of Insight Partners, was one of the all-time greats, having led rounds into Twitter and managing $90BN for Insight. Today, after 8 years of friendship, I released our episode and have gone over it to condense my biggest learnings from the discussion. 🚀 7 Lessons from Building a $90BN AUM Machine: 1. The Shift from Assistants to Employees 🤖 We are moving beyond "copilots." Autonomous agents aren't just tools; they are becoming digital employees with identities, credentials, and the authority to make decisions. If you aren't building your software to be used by agents, you’re building for a shrinking market. 2. "Cursor is Obsolete" 💻 Native AI startups are already moving past current coding tools toward homemade autonomous agents that write code directly. In AI, you can’t think about yesterday; you have to build for where the puck is going. 3. The Rise of the "Claw Stack" 🏗️ Just as the LAMP stack fueled the 2004 web explosion, Jerry predicts a new "Claw Stack" for agents. This involves an orchestration layer that triages workflows—sending high-reasoning tasks to models like Claude and Gemini, while routing simpler tasks to cheaper open-source models like Llama. 4. ASIC Chips > General Compute? ⚡ NVIDIA is king today, but the future might belong to ASICs. As models become more specific to workloads, we’ll see models put directly onto cheaper, more tunable chips. This is why Meta is betting big on their own silicon—they’re preparing for the ASIC explosion. 5. Selling to Agents, Not Humans 💸 The buyer is changing. When agents start buying software, pricing must shift to consumption-based models. An agent doesn't care about a "seat license"; it cares about the compute and memory required to get the job done. 6. Intuition vs. Wishful Thinking 🧠 Jerry’s biggest misses? Confusing wishful thinking with intuition. He’s learned that the founders who make you feel "comfortable" are often the ones who let you down. The best founders are often socially challenged, obsessed, and possess a "sharp edge" that makes them win. 7. Money Has No Instructions ⚡ Money is simply energy. It doesn’t come with a manual. As an investor or founder, your job is to respect that energy—don't waste it on the "middle," use it to back the crazy ideas that have the power to change the world. (Link in Comments) #founder #funding #business #investing #vc #venturecapital #entrepreneur #startup

That time I spent a morning with the future Billionaire, Melanie Perkins of Canva. It was September 2015, mid-morning at a much loved (since shut) live-music location, Humming Tree, in Bangalore. A small group of 20 gathered to meet the founder of a then 2-year-old platform. Canva would become a Unicorn 5 years later. Now worth $40 Billion. Melanie was shy, warm, and charmingly awkward – excited to meet Canva's power users in India, their second-fastest growing market after the US. We got free subscriptions, shared feedback, and when I mentioned my B2B AI design platform 'Outlined', her heightened curiosity gave me quite the thrill! While Outlined had its moments... -Web Summit Alpha Startup, - Confederation of Indian Industry India Design Yearbook feature - Our first paying customer ChildHope UK - Me as a podcast host featuring global Ai leaders on 'The CreativeAi Podcast' ...we never quite got past second gear. Without a technical co-founder and perhaps my own hunger not matching the vision, we rebuilt the product 4 times in 6 years. What Canva might've done in months, took us years. 3 learnings that are still relevant though: 1. Jump into the deep end. Don't let technical knowledge gaps stop you. I went from running a branding firm to managing technical teams. Today this is even easier with platforms like bolt.new 2. Build your mastermind group. Mentors aren't just grey-haired veterans – someone with 2 years of specialist experience in your blind spot can be invaluable. Peer mentorship is HIGHLY underrated. Seek advice from across age-groups. 3. Nurture those 'acquaintances'. Counterintuitively, acquaintances often help more than some friends in the early days – they're unburdened by preset notions, focusing purely on mutual growth. I'm sure Melanie has done all this and more in her time at Canva. And she shares her experiences quite openly on LinkedIn. 3 learnings about her brand on LinkedIn : - She writes from personal experience. She knows the importance of her position and speaks from a place of wanting to leave the world in a better place than she found it. - She often reshares her own articles and company initiatives from 5 or 10 years ago. Signaling the vision and timelessness of her observations. - She believes in communities. Be it their 1% pledge or giving away $1 Billion dollars worth of Canva access to NGO’s and educators to donating cash to alleviate extreme poverty. The same commitment to their team, having been ‘Great Place To Work® Australia’ certified. She cares. Some might say, "It's easier to 'care' when you're a Unicorn with thousands of employees." From afar, to me, the person you see on-stage at big Canva events is the same person I met in 2015. Warm, ambitious, sometimes awkwardly charming and ultimately on a mission to make a difference in the world. #canva #entrepreneurship #melanieperkins

What if you could save millions of lives by thinking differently about machines? Alan Turing did exactly that. In 1936, Alan Turing was a 24 year old mathematician at Cambridge asking a question that seemed purely theoretical. Could there be a universal computing machine capable of solving any problem that could be described as a series of logical steps? His answer, published in a paper titled On Computable Numbers, laid the mathematical foundations for every computer that would ever be built. Most people saw it as abstract mathematics with no practical application. Turing saw the future of computation. Then World War II changed everything. In 1939, Turing joined the Government Code and Cypher School at Bletchley Park. His mission was breaking Enigma, the encryption machine Nazi Germany used for military communications. The challenge was staggering.  Enigma had 159 million million million possible settings. Checking them manually would take longer than the war would last. Turing  built a machine that could think through the problem logically, eliminating impossible settings until the correct one emerged. The Bombe machine, as it was called, could break Enigma codes in hours instead of millennia. By the end of the war, Bletchley Park was decrypting thousands of messages daily. Historians estimate that breaking Enigma shortened the war by at least two years and saved millions of lives. But Turing was not satisfied with wartime applications.  After the war, he continued developing the theory of machine intelligence. In 1950, he published Computing Machinery and Intelligence, proposing what became known as the Turing Test. Can machines think? If you cannot tell whether you are conversing with a human or a machine, does the distinction matter? This paper became the foundation of artificial intelligence research. Turing never saw the impact of his work. In 1954, at age 41, he died from cyanide poisoning. The inquest ruled it suicide, though some historians question that conclusion. Today, the Alan Turing Institute serves as the UK national institute for data science and artificial intelligence, carrying forward his legacy. The tools you use every day, from smartphones to AI assistants, exist because one mathematician asked whether machines could think and then proved they could. When you encounter someone whose thinking seems too different, too unconventional, too far ahead of current understanding, ask yourself whether you are dismissing the next Alan Turing. What unconventional thinking in your organisation gets dismissed because it does not fit established patterns? #AlanTuring #AI #Innovation #Legacy #BritishHistory