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.

𝗠𝗲𝗲𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗠𝗮𝗴𝗶𝗰𝗶𝗮𝗻𝘀 𝗼𝗳 𝗔𝗜: 𝗠𝘆 𝗿𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 Over the last four months, I’ve had the rare privilege of meeting four of the biggest visionaries shaping the AI revolution:   🔹 𝗠𝗮𝘀𝗮𝘆𝗼𝘀𝗵𝗶 𝗦𝗼𝗻 (SoftBank Investment Advisers) 🔹 𝗦𝗮𝗺 𝗔𝗹𝘁𝗺𝗮𝗻 (OpenAI) 🔹 𝗝𝗲𝗻𝘀𝗲𝗻 𝗛𝘂𝗮𝗻𝗴 (NVIDIA)  🔹 𝗬𝗮𝗻𝗻 𝗟𝗲𝗖𝘂𝗻 (Meta) Each has an almost 𝗺𝗲𝘀𝘀𝗶𝗮𝗻𝗶𝗰 𝗮𝘂𝗿𝗮—not just because of their brilliance, but because of the weight of expectations on their shoulders. The world looks to them for clarity, direction, and answers. 𝗪𝗵𝗮𝘁 𝗦𝘁𝗼𝗼𝗱 𝗢𝘂𝘁 𝘁𝗼 𝗠𝗲? 𝟭. 𝗘𝘅𝘁𝗿𝗲𝗺𝗲 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 & 𝗖𝗼𝗻𝘃𝗶𝗰𝘁𝗶𝗼𝗻 These leaders don’t just believe in AI’s future—they are 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗶𝘁.  Each one is evangelizing their perspective on AI: -𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝘃𝘀. 𝗰𝗹𝗼𝘀𝗲𝗱-𝘀𝗼𝘂𝗿𝗰𝗲 -𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗿𝘆 𝘃𝘀. 𝗟𝗟𝗠𝘀 𝗮𝗿𝗲 𝗽𝗿𝗶𝗺𝗶𝘁𝗶𝘃𝗲 -𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝘃𝘀. 𝗱𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗔𝗜 -𝗖𝗼𝗺𝗽𝘂𝘁𝗲-𝗵𝗲𝗮𝘃𝘆 𝘃𝘀. 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆-𝗳𝗶𝗿𝘀𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 Their approaches differ, but they have 𝗮𝗯𝘀𝗼𝗹𝘂𝘁𝗲 𝗰𝗼𝗻𝘃𝗶𝗰𝘁𝗶𝗼𝗻. 𝟮. 𝗪𝗶𝗹𝗹𝗶𝗻𝗴 𝘁𝗼 𝗥𝗶𝘀𝗸 𝗜𝘁 𝗔𝗹𝗹 What's common to them is their 𝘄𝗶𝗹𝗹𝗶𝗻𝗴𝗻𝗲𝘀𝘀 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗯𝗶𝗴, 𝗶𝗿𝗿𝗲𝘃𝗲𝗿𝘀𝗶𝗯𝗹𝗲 𝗯𝗲𝘁𝘀. For some, the bet is on 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. For others, it’s on 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗿𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗶𝘁𝘀𝗲𝗹𝗳. Their 𝗿𝗲𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀, 𝗮𝗻𝗱 𝗯𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗱𝗼𝗹𝗹𝗮𝗿𝘀 are tied to AI’s success. 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗶𝘀 𝗮𝗻 𝗼𝗽𝘁𝗶𝗼𝗻. 𝗜𝗻𝗮𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁. 𝟯. 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗣𝗮𝘁𝗵𝘀, 𝗦𝗮𝗺𝗲 𝗗𝗲𝘀𝘁𝗶𝗻𝗮𝘁𝗶𝗼𝗻 While their risk appetite is high, their methods are vastly different:   💡 Jensen is 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 that fuels AI’s exponential growth  💡 Sam is 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 and pushing the world towards #AGI 💡 Masa is making 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁𝘀 to accelerate AI-driven businesses 💡 Yann is challenging 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗔𝗜 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺𝘀 to build a more robust path to AGI 𝟰. 𝗜𝗻𝗱𝗶𝗮’𝘀 𝗔𝗜 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗜𝘀 𝗼𝗻 𝗧𝗵𝗲𝗶𝗿 𝗥𝗮𝗱𝗮𝗿 Each of them sees 𝗜𝗻𝗱𝗶𝗮 𝗮𝘀 𝗮 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗽𝗹𝗮𝘆𝗲𝗿 𝗶𝗻 𝗔𝗜’𝘀 𝗳𝘂𝘁𝘂𝗿𝗲:   • Jensen has emphasized India’s rapidly growing developer talent • Sam has actively engaged with Indian AI startups and policymakers • Masa continues to place big bets on India's innovation ecosystem  • Yann has highlighted India’s potential in fundamental AI research India is emerging as a 𝗴𝗹𝗼𝗯𝗮𝗹 𝗔𝗜 𝗵𝘂𝗯 and a thriving startup ecosystem. 𝗪𝗵𝗮𝘁 𝗖𝗼𝗺𝗲𝘀 𝗡𝗲𝘅𝘁? The 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 won’t be shaped just by these visionaries.  It will be built by those willing to take bold bets, push AI research boundaries, and execute at scale We are just getting started! nasscom Fractal #India

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

• Born in Bratislava, moved to Canada young • BSc in Computer Science from University of Toronto • MSc from University of British Columbia • PhD at Stanford under deep learning pioneer Fei-Fei Li, focusing on connecting vision + language systems • Created legendary Stanford CS231n course that taught an entire generation CNNs and computer vision • Co-founded OpenAI and worked on early deep learning research • Recruited by Elon Musk to lead AI at Tesla Autopilot • Built large-scale production computer vision systems for millions of cars using end-to-end neural networks • Helped push the shift from hand-engineered rules -> neural network driven perception stacks • Coined the term “Software 2.0” -> replacing explicit code with learned neural weights • Coined “Vibe Coding” -> where humans describe intent and AI writes software • Famous for saying: “The hottest new programming language is English” • Built some of the most influential AI education content: Zero-to-Hero, nanoGPT, neural networks from scratch • Founded Eureka Labs to rethink AI education • Elon Musk once called him “arguably the 2nd guy in the world in computer vision” Researchers publish papers, Engineers ship products, Very few people redefine entire fields. Andrej Karpathy did all three

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.

“Age 41 was perfect. Before that, I lacked the scars; after that, I'd lack the stamina." This is Eric Yuan explaining why he founded Zoom. Not at 21 in a dorm room, but after 8 visa rejections and 14 years watching video calls fail. Here's what happened: Yuan arrived in Silicon Valley in 1997 after being locked out of the U.S. for two years. Nine visa applications. Eight rejections. He joined WebEx as one of the first 20 employees. For 14 years, he lived in enterprise video hell. Every dropped call. Every frozen screen. Every angry customer email at 3 AM. He spent 14 years learning exactly why video was broken. By 2010, as VP Engineering at Cisco: "The year before I left, I did not see a single happy WebEx customer." He pitched Cisco on a complete rebuild: smartphone-first, cloud-native. They said no. So at 41, Yuan quit. Time to bet it all. Within days, 40 engineers (5% of Cisco's collaboration R&D staff) followed him to a cramped office with equipment stacked on a fridge. His advantages weren't what VCs typically fund: - 14 years knowing why WebEx failed (not 14 weeks at a hackathon) - Fortune 500 CIOs who trusted him (not Stanford roommates) - Technical scars from real customers (not YouTube tutorials) - Patience to wait until his kids hit middle school (not dropping out) Dozens of VCs rejected him. He made "It can't be done" his screensaver. His first customers: Former Cisco contacts who'd seen him in the trenches. April 2019: IPO at $9B (one of the few profitable tech unicorns). 2020: Revenue up 326%. The real moat? Being experienced enough to know what was broken, and patient enough to fix it right.

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

At 34, she rang the Nasdaq bell, holding her toddler in her arms. A $1.6B valuation. Katrina Lake had just taken her company, Stitch Fix, public. A company she built from her apartment floor, while pregnant. Twice. She didn’t come from a family of founders. She didn’t have a network of angels or tech mentors to call. Her mother was a teacher who immigrated from Japan. Her father, a transplant doctor who believed deeply in hard work and quiet sacrifice. She grew up straddling two worlds - Japanese at home, American at school. Learning early that success didn’t come from confidence, but discipline. Years later, Katrina found herself in business school, surrounded by future bankers and VCs. But she had no interest in finance. She wanted to build something… useful. The idea started simple: What if busy women could get clothes picked out for them - based on data, but curated by a human? She used SurveyMonkey to collect style preferences. Tracked feedback in spreadsheets. Packed boxes from her apartment floor. And mailed them one by one. This wasn’t a “tech company” in the traditional sense. No SaaS margins. No app. No virality. Just a belief that personalization - done right - could scale. Most investors didn’t get it. Too operational. Too slow. Too… retail. And maybe, too female. So she ran lean. Proved retention. And let the numbers speak. By 2017, Stitch Fix was ready. She took it public, and made history. The youngest woman to IPO a tech company. A new kind of founder. One who didn’t “fit the mold.” She just built her own. A second-generation immigrant founder, raised to keep her head down - who ended up showing the world what leadership can actually look like. Quiet. Relentless. Human. And maybe that’s what more of us need to see right now.