Tag: Business Leaders

  • Access is a commodity. Capability is not.

    Access is a commodity. Capability is not.

    Buying AI has become easy. It is cheap, quick and open to anyone with a card. What separates the companies that gain from it is something else, and I found a useful way into that from defence, of all places.

    In October I wrote five short posts here on LinkedIn, one on each working day of a week. They began with two army leaders, one in India and one in the United States, and ended up being about how a company builds, buys and organises itself. This edition puts them together.

    Wars are not won by technology. The other side gets it too.

    General Dhiraj Seth, who took over as India’s Army Chief in June 2026, made much the same point at the NDTV Defence Summit in September.

    The advantage goes to whoever adopts quickly and has the capability to use what they have adopted. Dan Driscoll, until recently the US Army Secretary, presses a version of it from Washington. In his Foreign Affairs essay, “Rewiring the Arsenal of Democracy“, he argues that Western militaries must learn commercial speed and commercial iteration. Two armies, two continents, one idea.

    I have held this same view about AI in business for a while. Two companies buy the same tools, in the same week, at the same price. Deploying them proves very little.

    The gap shows up a year later, and never in the tooling. One has changed something its customers can feel. The other has a usage bill and a pilot nobody got around to shutting down.

    Access is a commodity. Capability is not.

    Every technology cycle has had some version of this gap. What is different now is how little it costs to buy, and therefore how little it separates anybody.

    Kipling did this arithmetic in 1886.

    In “Arithmetic on the Frontier“, an officer trained at enormous expense is brought down in a border skirmish by a cheap local rifle. The costly education loses to the inexpensive weapon.

    Enterprise software is having its frontier moment. A founder with a $100 monthly AI subscription can now build a good part of what once needed a $100,000 custom project and an army of outsourced engineers. The tender, the sprint plan and the offshore bench were all priced for a world in which writing code was the scarce thing.

    It no longer is. Those of us who built services businesses on that arithmetic might want to read the poem again.

    Cheap does more than shrink the budget line. It changes what’s worth holding on to.

    Drones are not the fighter jets of the last century, prized and maintained for decades.

    They are closer to bullets and shells: made in volume, used for the mission, expended.

    Driscoll puts it plainly in the same essay. “Bespoke platforms are luxury items.”

    Software is heading the same way. When AI can produce a working tool in days, code written for one need no longer has to be nurtured, documented and defended as a company asset. Build it for the job, use it, let it go when the need changes.

    IT services taught a generation of us to treat the code as the asset, to be maintained, versioned and billed against for years. Looking back at what my teams carefully preserved, we could rebuild a fair amount of it in an afternoon today.

    Some of it was genuinely an asset. Most of it was just old.

    Letting go of something you built is among the harder founder instincts to unlearn.

    Once what you build stops being permanent, the question of who builds it loosens as well.

    Enterprises spent years consolidating IT vendors, for scale, price and one throat to choke. Sensible, for its time.

    Driscoll argues the reverse for defence. An industry that had “devolved into an oligopoly” cannot produce the mass that modern war demands. He wants the wider industrial base, automotive and technology manufacturers included, able to plug in through open interfaces.

    AI opens the same door in the enterprise. A specialist can now deliver a module for one purpose, and that specialist may be small, distant, even a single person. The empanelled vendor list was drawn up for a scarcity that has gone.

    Not the whole list, though. The teams I see handling this well have kept a stable core for run-and-maintain and the regulated work, and opened a second lane alongside it for short, outcome-defined buys.

    The second lane is where most of the interesting work is going. It is also the one procurement has no template for.

    What gets built and who builds it are both procurement decisions in the end, and procurement makes them. How a company is organised is harder, because nobody can sign that into existence.

    The best AI tools do very little inside a company that runs on silos.

    Driscoll’s description of military systems will be familiar to anyone who has walked an enterprise floor: systems that cannot share data, each one a walled garden, impressive on its own and useless to anyone on the other side of the wall. His fix is open standards, mandated at the point of procurement. He reports that once the US Army connected hundreds of its business systems, it ran its first personnel audit in over fifty years.

    Companies have walled gardens too, and people keep the walls up. Sales, delivery, finance and support each hold a piece of the customer, and each one guards its piece. AI can assemble the whole picture only when the data flows across them, and customers notice when somebody finally acts on it.

    Mandating a data standard is the easy half, and the half a CEO can sign. The hard half is that people with good reasons hold up the walls. I doubt that shifts without fewer layers and reporting on demand, in place of reporting up the chain.

    Which is why the AI budget usually turns out to be the simplest line item in the whole exercise.

    Building has become cheap, so what gets built no longer needs to last. The list of people who can build it has widened. And how a company is organised determines whether any of it reaches the customer. None of those three comes with the subscription.

    None of this is an argument against buying the tools. The founders I see pulling ahead bought early, and then spent the year on everything the purchase did not decide: which problem was worth solving, who should build it, and whether the answer could travel from the people who had it to the people who needed it. The subscription purchase was the cheapest and quickest thing they did all year.

    General Seth observed that technology may be making battles shorter without making wars any shorter. AI is behaving the same way inside companies. The tools shorten the distance to a working demo. What the customer feels depends on everything else.

    Access is a commodity. Capability is not.

    If you are a founder growing into the seat where these calls land, my book The Founder Catalyst is a practical guide to that transition.

  • What Captain Picard taught this founder

    What Captain Picard taught this founder

    I met Captain Picard in the mid-1990s. Star Trek: The Next Generation aired on Star Plus in India. Often at odd hours. No replay.

    I missed many episodes. I saw enough to stay hooked.

    What I admired most was the man. Steady. Fair. Never loud. Over the years I found he had also taught me a few things about running a company.

    Here are the ones I still use.

    1. Listen first. Then decide.

    Picard rarely decided alone.

    He turned to his crew. “Number One?” “Mr. Data?” “Counselor?” He heard each view out.

    Then he said, “Make it so.”

    Both halves matter. Skip the listening and you decide blind. Skip the deciding and you freeze. I wrote about the freezing half in What Captain Kirk can teach founder CEOs. Picard is the other half.

    2. If they don’t get you, tell a story

    In “Darmok”, Picard meets a race that speaks only in metaphor. Their words are references to their own stories. Plain language fails.

    He bridges the gap by telling the Epic of Gilgamesh.

    Founders hit this wall often. A team, a customer or an investor just doesn’t get it. So we explain louder. We add more slides.

    The answer may be a shared story, not a better argument.

    I learnt this the hard way. When I let ChatGPT expand the outline of one of my talks, it came back polished but generic. It only came alive when I went back to my own stories.

    3. Build a crew that is not like you

    Look at his bridge. A Klingon. An android. A blind engineer. A counselor who senses feelings.

    None of them thought alike. That was the point.

    Hard problems need many angles. If everyone in your room nods together, someone is holding something back.

    4. Four lights

    In “Chain of Command”, Picard is captured and interrogated. His captor holds up four lights and tells him to say five.

    Picard refuses. “There are four lights!”

    Later he admits that, at the very end, he had begun to see five.

    No founder faces an ordeal like that. But we all feel a milder version of it.

    A board wants growth. An investor wants a bigger story. A team wants good news. So a number gets bent a little to please the room. Revenue. Runway. Pipeline.

    That is the fifth light.

    Four is four. Report what is true. And know that pressure bends even the best of us.

    5. Stay with what is in front of you

    In “The Measure of a Man”, Picard defends Data in a hearing. Data is an android. Is he a person, or property?

    Great television. Picard argues it superbly. What I take from him is his calm. He met a hard question with clear thinking, not fear.

    But Data is a character in a story. He is not a forecast.

    My view is simple. Don’t anthropomorphise today’s AI. It is not a being. It is not plotting anything. I use ChatGPT and Claude Code every day. They are capable assistants. Nothing more.

    The fear of machines taking over humans is, to me, an unnecessary worry. It comes from drinking too much science fiction Kool-Aid. The later series Picard built whole storylines on fear of synthetic life. Good drama. Poor business guidance.

    Founders have real questions today. What does AI do to my costs, my people, my customers, my community?

    Work on those. Leave the far horizon to the experts in the field.

    6. Your scars stay with you

    “Tapestry” is a Next Generation episode. As a young man, Picard picked a bar fight and was stabbed through the heart. He has lived with an artificial heart since.

    Now, near death, he wishes he could undo that reckless youth. A being called Q gives him the chance.

    He avoids the fight. And wakes up a careful, forgettable junior officer who never commands a ship.

    He begs to go back.

    The scar was part of who he had become.

    I carry one too. I was 14.

    I was on an NCC trek near Pune. I had never spent a night away from my parents. We walked over 30 kilometres a day across hilltops and forts.

    On one descent, I slipped. My canvas shoes made it worse. I slid fast towards a rock face several hundred feet below.

    A senior cadet from a city college caught hold of me. He stayed by me for the rest of that day. I never learnt his name. I owe him my life.

    The scar from that slip stayed with me for years.

    It still does. Whenever I have faced an impossible situation, with no way out, my mind goes back to that slope. To the fall. And to the hand that caught me.

    Every founder carries a scar like this. A moment that never quite leaves.

    It returns when the pressure is highest. Know yours.

    Then remember who caught you. A mentor cannot stop you from slipping. A mentor is the one who catches you when you do. In The Founder Catalyst, I wrote that “every founder needs a mentor — and every founder must also be one.”

    The scar reminds you of the fall. The mentor is why it was not the end. Be that mentor for the next founder.

    7. Optimism is a working tool

    Star Trek’s future is not free of trouble. Its people just assume trouble can be solved.

    That is not a mood. It is practical. A team that believes a problem is solvable will actually work on it.

    Star Trek shaped how I think about complex issues, more than I realised at the time.


    Sir Patrick Stewart has passed away at the age of 86. AI can now recreate a face and a voice. It will never be the real Patrick Stewart. For millions of fans like me, he will always be Captain Jean-Luc Picard.

    Thank you, Captain. Rest in peace, Sir Patrick.

  • Why Microsoft is offering exits now?

    Why Microsoft is offering exits now?

    What a voluntary exit at Microsoft may tell founders about AI and the size of organisations

    When a company the size of Microsoft offers voluntary exits to about 60,000 people, the number takes the headline. The more interesting part sits behind it.

    This is not a company in distress. It is highly profitable and holds strong cash reserves. A decision of this scale would also have been modelled carefully, with several outcomes weighed. So the question I keep returning to is why now.

    I can see three possible reasons, and they are not mutually exclusive. These are my own reading of the move. Microsoft, as reported by GeekWire, says the offer does not signal a need for fewer employees, and that may well be true today.

    The first reason is capital. Building and running AI infrastructure takes enormous sums, and lower operating costs free up some of it. The second is the shape of the workforce. AI is already changing how software gets written, and it could do the same for management and business functions. A company may be preparing for a time when it can run a larger business with a smaller team. The third is attention. Cloud and AI matter more to Microsoft with each passing year, and older areas such as Xbox, Windows or Office may not receive the same strategic focus.

    A voluntary exit also carries a quiet complication. The company does not get to choose who accepts. Those who were ready for a change will leave, and that helps simplify the organisation. But some experienced and highly capable people will leave too, often because they have the most options. And 60,000 is only the number eligible. How many actually accept is the figure worth watching, and we should know once the offer window closes in early December.

    I have seen the other side of this. Around 2008, during the subprime crisis, one of our largest clients in the USA cancelled their order almost overnight, and we lost over half our revenue. The next day I called a townhall and told the team we had to let go of nearly a hundred people. I promised a severance better than the statutory norms, even though it meant borrowing well beyond our usual limits. That decision was forced by circumstances, and the choice of who left was ours. Microsoft is in a very different position. It is acting from strength and handing the choice to its people.

    Yet the two situations share something. The size and shape of an organisation is never fixed. It is a series of decisions, and leaders are now making them with a new variable in the room.

    AI is not only changing the products companies build. It may also change how they organise themselves, how many people they need and where they choose to invest. For founders and business leaders, that question is worth thinking about today, and not five years from now.

  • When customers hack your business model

    When customers hack your business model

    Smart TV owners are using AI coding agents to strip ads and bloatware from their sets. TV makers depend on that ad income, and they will be tempted to lock things down. I think the better path is to own the problem. The lesson reaches well beyond televisions.

    On 17 October, a developer posting as Gathin shared how he fixed a family TV that showed an ad before Netflix would open. He pointed Claude Code at the Xiaomi set over home Wi-Fi. In one afternoon, it disabled 25 packages and wrote a clean new launcher. The project, now called CleanYourTV, passed 30,000 stars on GitHub within a week and lists over 240 models from 72 brands. Similar tools for Fire TV and LG appeared within days.

    It is easy to file this under hobbyists having fun. I would look closer. This is what happens when customers have a grievance and, for the first time, a way to act on it beyond just complaining.

    It is tempting to cast the manufacturers as villains driven by corporate greed. They deserve a fair hearing first. TV hardware has been a punishing business for years. A brand recovers its development cost in the few months after launch when it can charge a premium. After that, it is a price war, increasingly against well-made sets from Chinese brands. Ads, viewing data and home screen placements have become the recurring income that holds the model together. When Walmart agreed to buy Vizio for USD 2.3 billion in 2024, the announcement noted that Vizio’s advertising-led platform business already made up most of its gross profit. For many brands, the ads are the margin.

    Infographic titled 'When customers hack your business model' by Founder Catalyst, illustrating a new approach to consumer engagement with key sections: 'The Hack', 'The Mismatch', and 'The Choice'. It emphasizes ad-free experiences and addresses customer ownership perceptions.

    Owners see it differently. They paid full price for a TV that often feels slower after two years than on the day it arrived, with sponsored rows they never asked for. That is partly why people happily pay extra for an Apple TV box. They are paying to escape a home screen they already own. After a hard day’s work, all a viewer wants is to reach their show with as few hurdles as possible. A few years ago, an extra ad row might have been shrugged off. With rising household costs and streaming bills already piling up, even a small irritation can become the last straw.

    I expect most makers to reach for the familiar tools that big tech firms go for. Firmware updates that quietly reinstall whatever users removed, something CleanYourTV’s own documentation already warns about. Locking the Home button to the stock launcher, as Fire OS and LG webOS already do. Closing developer mode, a hidden settings menu meant for app developers, and with it ADB (Android Debug Bridge), the channel that lets a computer on the same network send commands to the TV. That is exactly how these AI tools get in. They’re rewording warranty terms so any modification ends support.

    Further out, I would not be surprised to see them lobby for the kind of legal protection Hollywood secured. In 1998, the studios helped push through a US law that made it illegal to get around copy protection, even on discs people had legally bought. They also made HDCP encryption on HDMI a condition for sending HD content to a screen. India added a similar anti-circumvention rule to its Copyright Act in 2012. After that could come startup software that only runs with a manufacturer key, sets that refuse to work after tampering, and streaming apps withdrawn from older sets sooner.

    Each of these makes sense in a quarterly review. Together they tell customers the TV was never really theirs. The likely result is more buyers choosing a plain display, which Chinese makers will gladly sell for even less, and plugging in a box from a brand they trust, like Apple TV. Newer entrants will follow, because the same AI that set off this commotion has also made it far cheaper to build such a product. The ad revenue goes anyway, and the goodwill goes with it.

    Another route exists, and it’s why I wrote this piece. Admit the experience has gone wrong. Offer existing owners an official update that removes ads and bloatware for a one-time fee, with a clear promise to replace any set it damages. Sell ad-free models with generous trade-in value on older ones, or a plan that lets customers upgrade every five years at a real discount. For enthusiasts, offer a paid tier with official AI access, an MCP server in today’s terms, or your own app that lets them customise the TV to their heart’s content without breaking it. Some of this costs money in the short run. It also turns the customers who have paid your bills all along, the very people now working around you, into your most loyal ones.

    Large companies have taken this route before. In late 2009, Domino’s ran ads in which its own customers called the crust cardboard, then showed the company rebuilding its recipe from scratch. In 2010, Apple answered the iPhone 4 antenna complaints by offering every owner a free case or a refund. In 2013, Microsoft dropped the Xbox One’s mandatory online check-in and usage restrictions within weeks of announcing them, before the console shipped. None of these was comfortable. Each one admitted a mistake customers already knew about.

    I learnt a smaller version of this at my own company. About two decades ago we built VPF, a content management system for large news and media groups, robust enough to serve millions of page views a day. We charged a licence fee on top of our development services, and clients resisted it. We stopped defending the fee and bundled the product free into our service engagements. It was the right call. In my book, The Founder Catalyst, I go a step further and admit that open sourcing it from the start might have served us even better.

    AI agents have given ordinary customers the power to rewrite products they already own. Televisions are simply the first place it became visible. Any business whose margin depends on customers putting up with something they dislike is now on borrowed time. It is wiser to stop relying on that patience before customers decide for you.

  • A talk I have been living for thirty years

    A talk I have been living for thirty years

    Some talks you write in a few weeks. This one, I have been living for thirty years.

    On 28 August, I will be speaking at TEDx Thiagarajar College of Engineering in Madurai.

    I am not sharing the title here. That stays for the room. But if you remember Easytools, you already have half the hint. The other half is AI.

    There is something fitting about going back to an engineering campus for this. My own years in electronics and communication engineering, some 400 kilometres away in Chennai, are where most of what I will talk about actually began. Not in a classroom, but in the freelance projects I took on for local clients while still a student, writing software for people who needed something to work, not something to impress.

    Long before any of that, there was a publishing house. My grandfather started it in 1929, and my father ran it for over fifty years. I grew up around the hum of printing presses and a very specific kind of discipline, the kind where if a customer found a printing error in a book bought years earlier, you replaced it without a single question asked. I did not think of it as a business lesson at the time. It was just how our house worked.

    A year after college, I built my first product, a shareware security app called EasyPass. It found buyers in over fifty countries, which told a twenty-something engineer in Chennai something he had no real way of grasping before then, that the internet did not care where you sat while you built something. It also taught me the other lesson early, the uncomfortable one. A collaborator copied the code and sold a version of it under his own name. Setbacks and success, I learned, tend to travel together from day one.

    By the late 1990s, that first product had grown into Easytools.com, freeware and shareware sold online through Vishwak Associates, the company that would later become Vishwak Solutions, at a time when most Indian businesses were still deciding whether the internet was worth taking seriously, and a dial-up connection here involved a government department in one way or another. Nobody called what I did a startup then. Nobody called it product-led growth either. I was just trying to get software into people’s hands around the world, using whatever the internet allowed at the time, and carrying forward a habit I had absorbed without knowing it, that you own the problem fully and never make the customer feel at fault for it.

    Three decades on, I am still working through some version of the same question, just with faster internet and higher stakes.

    If you are around Madurai on the 28th, I would love to see you there.

  • When AI replaced the product

    When AI replaced the product

    Many founders in India may not recognise the name Chegg. Yet, for nearly two decades, it was one of the most successful education technology companies in America.

    Founded in 2005, Chegg built a subscription business around helping students with textbook solutions, homework assistance and tutoring. For millions of college students, it became a daily habit. If you had a difficult assignment, chances were you searched Google, landed on Chegg, and paid for access to the answer.

    Then came ChatGPT.

    Unlike many businesses that found AI to be another productivity tool, Chegg faced something far more fundamental. Students no longer needed to search for answers. They could simply ask an AI assistant. The experience was faster, conversational, and often free.

    The change in user behaviour happened much faster than anyone expected.

    The numbers reflect that shift. Chegg’s revenue declined from about US$767 million in 2022 to about US$377 million in 2025. During the same period, the company went through repeated restructurings, reduced its workforce significantly, closed offices and began searching for new growth areas beyond its original business.

    To be fair, AI was not the only reason. College enrolments softened in some markets. Textbook usage changed. Google’s AI-generated search summaries reduced traffic to many educational websites. Competition increased. Even Chegg acknowledges that these forces arrived together.

    What makes Chegg’s story remarkable is not that a company struggled. Businesses have always faced disruption.

    What makes it remarkable is that its core product was displaced so directly.

    In many industries, AI helps employees work faster, write better code, analyse data or improve customer support. The business still exists; AI simply makes it more efficient.

    For Chegg, AI became the product that customers wanted instead.

    That distinction is important.

    Looking back, Chegg will probably be remembered as one of the first large, publicly listed companies whose primary value proposition was overtaken by generative AI within a remarkably short period.

    Whether the company successfully reinvents itself remains to be seen. It is still operating, investing in new businesses and attempting to find its next chapter. But its journey is already an important business story.

    Technology disruptions are usually discussed in hindsight, years after they unfold. This one is happening in front of us.

    Twenty years from now, when people write about the early impact of generative AI on business, I suspect Chegg will occupy a chapter of its own.

  • 8 real world founder fears in the age of AI – Podcast Video

    8 real world founder fears in the age of AI – Podcast Video

    A D2C founder running an Ayurvedic skincare brand, doing about 8 crore in revenue, put it simply. AI is helping everyone create ads, write product descriptions, launch stores faster. My fear is not the technology. My fear is that AI makes every brand look equally good. If everyone has the same marketing superpower, where does my differentiation come from?

    Vineeth Vijayaraghavan, who I have known for over thirty years and who advises VCs, family offices and startups on AI strategy, did not pretend there was a tidy answer. This needs introspection, he said. But he had a reframe worth sitting with.

    Most of us think about AI inside a business as either software or marketing. Those are the obvious places. What he is seeing some founders do instead is use AI to build capability they could never have afforded before, inside the core of the business itself. He gave the example of a chemical engineering company where the founder now has six AI agents working alongside him on R&D, something that used to require hiring people the business simply could not access or afford. The same logic applies to a skincare brand. AI for product research and literature review is not a bad place to start, he said, though obviously with a healthy dose of scepticism, especially in a category where claims need real verification.

    The differentiation question does not disappear. But it shifts from what does my marketing look like to what am I now capable of building that I was not before.

    This was one of eight real founder fears we worked through on a new episode of The Founder Catalyst, recorded this month in Chennai. Instead of the usual interview, we sourced questions directly from founders across industries and went through them one by one, no scripts, no theory.

    A custom software services founder in Coimbatore is not worried about his developers using AI. He is worried his US clients will stop asking for large projects and instead ask for one senior engineer doing the work of five, slowly turning his company into a staffing business with shrinking margins. Vineeth’s answer, the volume of smaller, faster engagements is going up industry-wide, and the founders doing well are the ones who can turn around small projects quickly and let those grow into larger ones over time, rather than chasing fewer, bigger contracts.

    A recruitment firm placing engineers for startups fears that AI screening and ranking candidates makes recruiters irrelevant. Vineeth flipped this one. As AI competency becomes harder to evaluate, not easier, because a test or a hackathon no longer tells you much, the recruiter who has spoken to hundreds of candidates and can spot what is real becomes more valuable, not less.

    A chartered accountancy firm is not afraid of losing clients. Being a regulated profession, that part feels safe. The fear is clients expecting the same work for half the fee. Vineeth’s response, look at firms already doing this well. He described one CA firm in Chennai that grew from 14 to 60 people, with AI handling the routine compliance work and a small number of AI-native young developers building internal tools, while the chartered accountants focus on the judgment calls AI still cannot make. That firm, he added, is now raising serious investment, including from outside India.

    We also went through fears from an automobile manufacturing unit near Hosur worried about larger competitors using AI to out-pace them on pricing and forecasting, a legal services firm worried about how junior lawyers get trained if AI does their early work, and a logistics company with 70 trucks wondering whether technology becomes the real competitive edge over operational excellence.

    We closed with an imaginary scenario. Three founders in a room, a SaaS founder doing 10 crore in ARR, a services founder with a hundred people, and a manufacturing business doing 50 crore. Which one should be most worried about AI, and which the least? Vineeth’s answer was not about industry at all. It was about culture, and the willingness to act.

    Watch the full conversation here:

    If you are a founder or business leader trying to make sense of what AI actually means for your business, beyond the hype, this conversation is for you.

    About the guest:

    Vineeth Vijayaraghavan advises companies, family offices, and VC boards on technology and investment strategy, with a particular focus on AI.

    About the host:

    Venkatarangan Thirumalai is the author of The Founder Catalyst and writes and speaks on AI, leadership, and founder strategy. More at thefoundercatalyst.com

    When I work with founders on this, the conversation rarely starts with the technology. It starts with what people are afraid of losing, and whether that fear points them toward the right question or the wrong one.

  • Your Learning Loop Is Your Real Moat

    Your Learning Loop Is Your Real Moat

    Over the weekend, Microsoft CEO Satya Nadella shared a note on what he believes will define the next era of AI: organisations that “own their learning loop”.

    In simple terms, his point is this: the real advantage in AI will not come from access to models alone, but from how effectively a company captures, learns from, and improves using its own experience.

    For founders, this is worth paying attention to.

    Skills, prompts, and agents are portable. You can switch models, replace tools, and migrate clouds. None of these compounds on its own.

    What compounds is the feedback loop around your business: the traces from real work, the evaluations that measure what matters, the preferences that capture human judgement, and the outcomes tied to business results.

    Every customer interaction, every workflow, every decision can make your organisation smarter — if you capture the signal.

    That is why owning your learning loop matters. Not because it locks you into a platform, but because it builds institutional knowledge that outlasts any model, framework, or vendor.

    Today, trace data is converging around standards like OpenTelemetry. Evaluations remain fragmented across frameworks and providers. The next piece of infrastructure we need is an open, portable standard for evals — one that works across clouds, local models, and agent frameworks, with businesses owning the data and the format.

    Your learning loop is your IP. That’s the advantage that compounds.

    Infographic on Satya Nadella's own your learning loop idea: what compounds (the feedback loop of traces, evaluations, preferences and outcomes) versus what doesn't (skills, prompts and agents), plus the case for an open, portable evaluation standard

  • Why a papal letter on AI deserves five minutes of your time

    Why a papal letter on AI deserves five minutes of your time

    Founders and CXOs spend most of their time watching what customers, competitors, and investors think about AI. Fair enough. But occasionally, a voice from outside the industry frames the conversation in a way that cuts through the noise. This week, that voice came from an unlikely place — a holy one.

    Pope Leo XIV released a roughly 200-page encyclical on artificial intelligence, with technical input from Christopher Olah, co-founder of Anthropic. The AI industry will not alter its roadmaps because of this. But the previous five encyclicals — on workers’ rights, Cold War peace, birth control, economic inequality, and the environment — each shaped public discourse in ways that outlasted the news cycle. They framed what mattered before the rest of the world caught up.

    I’ll be honest here, I went in sceptical. A papal letter on AI felt ceremonial at best. The New York Times summary I read changed that.

    1. Regulation. Not self-governance by the industry. Actual government regulation of the private companies driving AI development. That distinction matters.

    2. Worker protection. Retraining and safeguards for workers displaced by AI. Already unfolding in IT services, shared services, and process-heavy roles.

    3. Education. Helping students think critically about AI, not just use it. A generation that consumes AI output without understanding its limits is a business risk, not just a social obligation.

    4. Children. Protecting children from AI-generated violent, hypersexualised, or false content. Not a future risk — anyone with a teenager at home already knows this. But there is a business dimension here that leaders underestimate: if the next generation’s trust, emotions, and judgment are shaped by unchecked AI content, brand loyalty and consumer relationships become harder to build. That has real bottom-line consequences well within the decade.

    5. Weapons. Humans must remain responsible for all decisions involving weapons. The encyclical’s argument is direct: autonomous systems make war more feasible and harder to control, which undermines the principle that the use of armed force is a last resort.

    The practical takeaway for business leaders: four of these five are already your problems. Workforce displacement, critical thinking in your teams, child safety in your products, and the governance gap between what AI can do and who is accountable when it goes wrong. The Vatican did not invent these concerns. It just wrote them down in one place.

    Worth your time to read this.

  • What money alone cannot build?

    What money alone cannot build?

    “For founders, the question is which part of that transformation requires something that money alone cannot build.”

    This line stayed with me.

    Every founder I speak to right now is asking the same question: what should I build?

    Frontier AI models are moving fast. What looked like a defensible product six months ago feels like a feature today. Advice is everywhere, and most of it is too generic to act on.

    Then I read something that actually answered the question directly. S. Somasegar and Rasik Parikh at Madrona Venture Group cut through the noise in a way most commentary does not. Here is how I read it.

    Three areas where founders can still build with conviction.

    1. Own the liability, not just the tool.

    Frontier Labs will build powerful general-purpose systems. They will not absorb the legal, regulatory, and financial accountability that comes with specific outcomes in specific industries. I have watched Indian IT firms lose deals on exactly this gap — clients want someone to sign off and stand behind the result, not just ship the capability. That willingness to own risk is where durable margin lives.

    2. Build on data others do not have.

    Models are trained on what is public. The edge sits in what is not — internal workflows, edge cases, decisions, and historical context. When I built Simpligic years before GenAI was a term, the insight was the same: the moat lives in private data. If your product compounds with every customer interaction, you are in a territory the model labs have no roadmap to enter.

    3. Go deep into messy workflows.

    Some problems are not solved by more intelligence. They are solved by understanding how work actually gets done — across people, exceptions, approvals, and institutional habits built over decades. At Vishwak, the client relationships that held through every disruption were the ones where we were embedded in their delivery model, not just their vendor list. A two-year integration into how a firm actually operates is not something a foundation model company will replicate. There is no incentive to.

    Here is the framing I keep coming back to from the piece: the difference between a tool and an outcome. A tool makes a task easier. An outcome means you are on the hook for the result. Most of what is being built right now — including a lot of what gets funded — is tools. The businesses that will matter in five years are the ones that own the outcome.

    The question founders should be asking is not whether their product needs AI. That is the wrong filter. Soon enough, everything will. The more useful question is whether the solution requires something AI cannot supply on its own — a regulator who trusts you, data that never left your client’s firewall, a workflow only you understand end to end, or accountability that someone has to sign their name to.

    The article makes one more point that deserves to land. This buildout — hyperscalers, foundation models, the entire stack — is a multi-trillion-dollar bet that AI will transform every industry. That bet will pay off. But transformation and value creation are not the same thing. Value will concentrate in the places where technology alone is not sufficient. That is the territory worth building in.

    Full credit to Soma Somasegar and Rasik at Madrona for articulating this clearly. Soma is someone whose thinking I have followed for a long time — I first met him in 2005 when he led the Developer Division at Microsoft — someone who has always taken the long view, and has usually been right about it.l.

    The article from Madrona is here.


    If you are a founder stress-testing where to build, this is the kind of question I regularly dig into with the leaders I work with. Reach out at thefoundercatalyst.com or connect with me at linkedin.com/in/venkatarangan

  • The CIO had a record year, but her teams didn’t notice.

    The CIO had a record year, but her teams didn’t notice.

    The CIO had every reason to be pleased. She wasn’t.

    Board visibility. CEO approval. Record numbers of internal apps shipped. Her IT team was delivering at a pace that would have been unthinkable twelve months ago.

    When I joined the call, I expected good news. I heard hesitation instead.

    It took me a few minutes to notice what was missing from her update. She never mentioned the line managers. Not once. I asked her directly: Were the department heads actually seeing productivity gains from these applications?

    “Venkat, you guessed it. That’s exactly my problem.”

    Her team had been shipping AI-built internal applications continuously for nearly a year. Every department was requesting bespoke tools — automation utilities, reporting dashboards, workflow agents — for specialised use cases that previously would never have cleared a development backlog. The economics had changed. AI made these builds fast and cheap. Requests kept coming. Delivery kept pace.

    But the telemetry told a different story. Usage in single digits. For large teams where even ten percent adoption would register clearly, the numbers were flat.

    The apps were built. They sat unused.

    This is not a story about AI failing. It is a story about a safeguard disappearing.

    Before AI entered the picture, cost and engineering bandwidth acted as a natural filter on internal tool requests. Most ideas died quietly in the queue — not because anyone evaluated them and said no, but because the cost of building was real and the wait was long. That friction, however crude, forced a basic form of demand validation. Only requests with genuine organisational pull survived it.

    AI has removed that friction almost entirely. And most organisations have not replaced it with anything.

    What happens next is predictable. A manager spots a genuine gap — usually a real one. A request goes in. The tool gets built without meaningful involvement from the people who would actually use it. No end-user consultation. No workflow integration thinking. The manager accepts delivery. The team quietly returns to what they already know.

    The app joins a growing collection of well-intentioned software that nobody opens.

    The frustrating part is that the productivity gains from AI are real, and the evidence is unambiguous. EY’s chief economist, citing Federal Reserve Bank of St. Louis data at Davos 2026, found that generative AI is already saving workers the equivalent of 1.6% of total work hours. A randomised experiment with 1,174 adults (NBER, 2026) found AI closed nearly three-quarters of the productivity gap between higher and lower-educated workers. And a University of Hong Kong study (2026) found that untrained AI access actually hurt performance, while even a brief training intervention significantly improved both adoption and outcomes. The technology delivers. The variable is always the people side.

    I have seen this pattern repeat across organisations, and it connects to something I write about in my book. Twenty-five years ago, I built inventory software for my father’s publishing firm — convinced that the clear benefits would drive adoption on their own. The staff resisted. Not because the tool was bad, but because nobody had brought them into the process. The breakthrough came only when the firm’s general manager, a man deeply rooted in the old ways, took a personal interest in learning the system. His endorsement shifted the entire team’s behaviour. The technology did not change. The human dynamic did.

    That lesson has not aged. If anything, AI has made it more urgent.

    Speed is easy to measure. Adoption is what moves the business
    Speed is easy to measure. Adoption is what moves the business

    Most AI tool deployments follow the same top-down pattern. A business leader identifies the gap. IT builds the solution. The actual users — the ones whose daily habits need to change — are consulted last, if at all.

    Users in large organisations carry significant inertia toward familiar tools and established routines. That inertia does not dissolve because a new application exists. It dissolves through involvement, through relevance, and through time. None of those three things gets faster just because the build cycle did.

    The fix is not more training sessions or better change management communications. Those are responses to a symptom.

    The deeper problem is that the old economic forcing function has gone, and nothing has taken its place. What needs rebuilding is a lightweight governance layer. Before any internal AI tool gets approved, two questions should have clear answers.

    Is there genuine pull from the people who will use it — not just from the manager who requested it?

    Does an existing tool or workflow already adequately cover this need?

    That gate does not need to be bureaucratic. It needs to exist.

    AI has made building cheap. It has not made building the right thing any easier. If anything, it has made that harder — because the friction that used to quietly filter out weak ideas is gone, and most organisations have not noticed yet.

    Speed of delivery is easy to measure and tempting to celebrate.

    Depth of adoption is what actually moves the business.


    If questions like these sit at the intersection of technology and leadership for your organisation, this is what I focus on at The Founder Catalyst. Happy to continue the conversation in the comments or connect directly.