Category: Advice & how-to

  • Stop Writing Code. Start Defending Decisions

    Stop Writing Code. Start Defending Decisions

    Last month I was on stage at Hotel Ramada Plaza, Guindy, for FICCI’s Digital Disruption and Transformation International Summit 2026 — part of FICCI’s 100-year celebrations. The panel was called “How We Build the Future & Lead with AI?”, moderated by Chandrashekar Kupperi, Founder of ANOVA M&A and Fundraising Advisory, with a lineup covering talent, leadership, MSME, and the future of work.

    I only got asked two of those four — talent and leadership — plus a sharp follow-up from the audience on hiring. That was enough to make me say some things out loud that I’d only written about before. Here’s the expanded version.

    Talent: the learning loop, and a question I’m not going to answer

    Chandrashekar’s first question was built on something I wrote a while back — that an organisation’s real advantage in AI isn’t access to models or tools, it’s the ability to keep learning. Before I answered, I gave the room a question instead of a point, and I’ll do the same here.

    Imagine it’s 2036. A new hire joins your organisation. Ask them to write a proposal — they’ve never opened Word. Ask for an analysis — they’ve never touched Excel. They’ve never opened a PDF to read one. I’m not saying that’s good or bad. I’m saying it’s close enough that talent conversations have to start accounting for it, and most don’t.

    Now, the learning loop itself. Satya Nadella has talked about this, and it comes down to one observation: today, the questions we ask AI carry as much intelligence as the answers we get back. Intelligence agencies have known this for decades under a different name — metadata. Who you talk to, when, how often, tells you almost as much as what was said. The same is true of our prompts. And right now, almost all of that signal walks straight out of the building. We ask, we get an answer, we move on. If the AI company retrains on it, fine — assume they don’t, even. Either way, your organisation keeps none of it.

    Compare that to how learning used to work. A fresher joins, does something, gets it wrong, a senior corrects them — and that correction becomes both the individual’s judgement and the organisation’s institutional memory. AI is now doing a growing share of that work, which means that correction loop, if you don’t design for it deliberately, simply stops happening. The knowledge stays trapped in the AI layer instead of compounding inside your company.

    There’s no off-the-shelf product that fixes this yet. But there are things you can start doing today. Singapore’s Foreign Minister, Vivian Balakrishnan — also, remarkably, a trained orthopedic surgeon — has built himself what he calls a second brain, and put his own working documents into it so he can query his own accumulated judgement. Look up his talk on it; it’s worth twenty minutes. I built something similar for myself, which I call an LLM Wiki, based on Andrej Karpathy’s original idea — LLM plus Wiki. If you want one, you don’t need to build it from scratch: open Claude or ChatGPT, ask it to build you an LLM Wiki, feed it your own documents and emails, and it’ll walk you through it, running entirely on your own machine. Enterprises need a more custom version of this — the frameworks are still evolving — but individuals can start now.

    If you’re serious about building this loop, it comes down to four Cs:

    1. Capture what you’re doing with your AI systems.
    2. Correct it — flag what was right, wrong, or almost right.
    3. Curate and codify it into something reusable.
    4. Circulate it through the organisation.

    If you’ve studied control systems as an engineer, none of this is new. It’s the same feedback loop, applied to how a company learns.

    Your learning loop is your real moat. Skills, prompts, and tools are rented — the feedback loop around your business is the only thing that compounds.

    Leadership: use AI as a cycle, not as a car

    The second question was about where a leader draws the line as AI agents get more capable of deciding and acting on their own. Before I answer that, credit where it’s due — the framing I use belongs to Dr. Pratyush Kumar, Co-Founder of Sarvam AI, from an interview worth watching in full. He draws a parallel between what’s happening in AI today and the old East India Company pattern: raw cotton taken out, spun into textiles elsewhere, sold back at a markup. Data and usage flow out; intelligence comes back priced. His conclusion from that: use AI as a cycle, not as a car.

    Don’t take the wrong lesson from that line. It is not an argument for slowing down. When you can’t out-race the competition, the tempting shortcut is regulation that brings everyone down to your speed — and as a nation, that’s a losing move. The point of the cycle is narrower and more useful: on a bicycle, your hands stay on the handlebars and your feet stay on the pedals. You’re still steering, at speed. That’s the posture leaders need with AI — not less speed, just retained control of the direction.

    Practically, this comes down to three Ds:

    Delegate. Hand off anything routine and repeatable — the tasks where you’re not applying judgement, only time. Writing Excel formulas used to cost you half a day of trial and error; today AI writes it in five seconds. Extend that all the way to code. I don’t think humans should be writing code by hand much longer — we’re not particularly good at it, and machines should be writing machine code. There’s a framework going around called “Caveman” — literally instructing the AI to drop the verbosity, the adjectives, the padding, and answer you the way a caveman would talk. Worth Googling. It’s a good instinct: most AI verbosity today exists because token-maxing is profitable for the companies selling you tokens, not because you need 200 pages for a two-word question.

    Decide. Once the routine work is delegated, the human’s job shifts to choosing between the options AI surfaces — not doing the legwork to get to those options. Today you have to go and click through everything before you can even decide. It should work the other way: AI opens your day, tells you what’s come in, lays out the decisions in front of you with the pros and cons, like a menu — and you choose.

    Defend. This is the one leaders skip, and it’s the one that matters most. Responsibility for an AI-assisted decision stays with the human who made the call — full stop. I envy auditors, lawyers, and doctors for exactly this reason: they need a license to practice, so accountability is built into the profession. Software engineering never required one — anybody can call themselves a software engineer, and now AI can write the code besides. IEEE has argued for fifty years that software should be a licensed discipline; it probably never will be. But whether or not the industry licenses it, the standard should still apply: when something an AI did goes wrong, you as the leader call the person responsible and ask them to defend it. “I used AI and it failed” cannot be where that conversation ends — the learning from that failure has to go back to the individual, back to the organisation, and back into the AI system itself. Which brings you right back to the learning loop.

    Delegate, decide, defend. That’s it. There’s no contest between you and AI here — the same way none of us think of a computer or a phone as competing with us at work. AI will get there too.

    The audience question that actually proved the point

    The last question came from the floor, not from Chandrashekar: with AI doing so much of the entry-level work, are we setting the bar impossibly high for people just starting out?

    I answered with a story instead of a theory. A couple of months back I was interviewing startup interns from VIT Bhopal, including two students from a state programme for high-achieving rural students — students who’d studied in Hindi, with limited English, and had touched a computer for the first time only in 11th grade. I told them upfront: show me your approach, don’t explain the code line by line, because I already know AI wrote most of it.

    One of them had taken a genuinely different approach to the problem. I asked how he’d landed on it. He told me AI had surfaced two candidate algorithms, he’d gone and read the actual papers behind them himself, and then implemented it in Python and demonstrated it working. That’s the part that matters — not that AI wrote the code, but that he’d gone one level deeper than the tool gave him.

    Four years ago, at the same stage of education, that same student would have failed a basic Python question outright, and I’d have written him off. He had a 98% in maths and could explain the algorithm clearly — AI hadn’t replaced his thinking, it had given him a running start he’d never have had otherwise. That’s the empowerment case for AI, and it’s the one we undersell.

    Which is also my answer to the IT industry’s old habit, the one I’ve watched for thirty years: hiring people to code without asking whether they understood the problem they were coding for. Those jobs are gone, and they’re not coming back. What’s left — the part that was always the actual job — is understanding the business problem well enough to direct the machine that writes the code. So no, we’re not setting the bar too high. For the first time, we’re finally asking the right question of people two years into learning computers.

    This piece is based on my session at FICCI’s Digital Disruption and Transformation International Summit 2026, on the panel “How We Build the Future & Lead with AI?”, moderated by Chandrashekar Kupperi. Video of the full session is on my YouTube channel.

  • AI and the polite Instagram hack

    AI and the polite Instagram hack

    A few days ago, hackers took over high-profile Instagram accounts, including the Barack Obama White House profile. And they did so by politely asking Meta’s AI support chatbot to update the email addresses on those accounts.

    The attack method was childishly simple. No sophisticated exploit, no back-end breach — and that was the shocking part.

    Meta had given its support bot the ability to reset passwords and manage account recovery. The bot was not mature enough to distinguish between the actual account owner and a fraudster using a VPN and a politely worded request.

    When we connect an AI agent to our CRM, our HR system, and our finance workflows, we are not just giving it the ability to read. We are giving it the ability to act. This ability to act is what makes agentic AI the most powerful software technology we have in recent decades. But at the same time, we need to ask ourselves: if someone prompted this agent cleverly, what could they get it to do?

    To me, the takeaway from this Meta incident is simple. Any access to a critical system needs to follow the same protocols that a manual process would follow. In fact, I would require the AI agents to follow even more stringent ones.

    And if there is an irreversible action or a decision involving critical business systems, then there needs to be a human checkpoint — not as a backup, but as a design requirement. At least for now, while we are still developing the understanding and the systems to manage what these autonomous agents can trigger.

    Think of the early web. Enterprises did not open their corporate systems to the public internet overnight. There was a long, cautious period of learning what the exposure actually meant before the architecture caught up.

    None of this is an argument against agentic AI. The potential is real, and I believe in it. But bleeding-edge technologies do exactly what the name suggests — they spill blood before they stabilise. The real question is whether your business can handle the loss. Mega corporations like Meta can. Can yours?

    As for me, I will keep reminding myself: how fast we roll out AI matters, but how robustly we build matters just as much.

  • 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.

  • People Remember The Sequence

    People Remember The Sequence

    Over 92,000 technology jobs have reportedly been cut globally in the first few months of 2026 alone. Meta, Microsoft, Amazon, Oracle, Salesforce, and several others are restructuring teams while simultaneously increasing spending on AI infrastructure, chips, and AI talent.

    None of this surprises me.

    AI is not another software upgrade cycle. It is a structural shift. Entire layers of work are being compressed, automated, or redistributed. Every founder and CEO knows this privately, even if many are still hesitant to say it publicly.

    What surprised me recently was not the layoffs themselves, but the sequencing around them.

    Meta’s internal memo explaining its restructuring reportedly arrived the same day thousands of employees were already being let go.

    That detail stayed with me.

    Because during moments of uncertainty, employees rarely remember the exact wording of the memo. They remember the order in which things happened.

    The technology industry often speaks about AI strategy as though it sits above everything else. I increasingly think the opposite is true.

    A company’s people strategy shapes its AI strategy.

    Not the reverse.

    This distinction matters because most large organisations today are not merely introducing new tools. They are redesigning trust structures inside companies. Reporting lines are changing. Teams are shrinking. Managerial layers are disappearing. Expectations around productivity are quietly being rewritten.

    And all this is happening while leadership teams themselves are still figuring things out in real time.

    I faced a version of this during the 2008 global financial crisis.

    One of our largest clients in the United States cancelled their engagement almost overnight. More than half our revenue disappeared immediately.

    In The Founder Catalyst book, I wrote about that moment:

    “When I called my team members in the USA, their reports were stark: empty shopping malls and vacant car parks. I knew instantly this wasn’t a temporary blip; it felt like a tectonic shift.”

    I remember returning home and realising we could not afford the luxury of waiting for complete clarity.

    The very next day, I called for a town hall meeting.

    Honestly, I did not yet have all the numbers worked out. But uncertainty grows faster in silence than in bad news.

    We had to let go of nearly a hundred people. At the same time, we stretched ourselves financially to provide severance beyond the statutory requirement, even borrowing heavily to do it.

    “It was painful, undoubtedly, for everyone involved, especially those leaving. We simply had no other choice to ensure the company survived.”

    Nearly two decades later, some of those former colleagues still greet me warmly when we meet or reconnect online.

    That stayed with me more than any balance sheet from any year.

    The AI transition will create extraordinary companies and extraordinary wealth. I have little doubt about that.

    But I also think the companies that emerge strongest from this phase will not merely be the fastest adopters of AI.

    They will be the ones who understand something older and simpler:

    People remember the sequence in which leaders treated them under pressure.

    Reflection: When restructuring under pressure, what you do first matters as much as what you decide. The order in which you treat people is the signal your organisation will remember long after the financial results are reported.

  • 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.

  • The quiet work of building a safe firm

    The quiet work of building a safe firm

    Some news is hard to read.

    Over the past week, two of India’s most respected IT firms have been in the headlines for reasons none of us wants to associate with the industry we grew up in. Tata Consultancy Services is facing a serious investigation in Nashik, with multiple police complaints, arrests, and a formal inquiry ordered by the Tata Sons Chairman himself. Infosys is responding to allegations surfacing on social media about its Pune operations. Both have shaken the industry.

    Having spent three decades in this space, I have no doubt the veterans at both firms, known for their integrity, will follow due process and address whatever is found. That is not what I want to write about today.

    I want to write to the young founders and early-stage CEOs who read my posts.

    Because the lessons here are not really about large firms with 100,000 employees. They are about what happens in your firm of twenty, fifty, or two hundred people, long before any headline is possible.

    The instinct that quietly hurts young firms

    When I started out almost three decades ago, I used to take every piece of misconduct in my firm personally. Thankfully, there were not many. But when something did happen, I felt it reflected on me as the founder. It took years, and guidance from my mentors, to understand that this view, while well-meaning, was not quite right.

    No founder can build a perfect firm. We must aspire to one and keep working towards it. What matters far more is whether your people have a safe, trusted way to raise concerns, and whether you act on them the moment they come in. That single question, asked honestly, will tell you more about your culture than any town hall ever will.

    In The Founder Catalyst, I devote an entire chapter to the idea that trust is the quiet currency of a business. It does not appear on any balance sheet, but it underwrites everything else. It is earned slowly, through small repeated acts of doing the right thing, and it can be damaged quickly, sometimes by a single moment of looking the other way. I wrote about a travel agent I had used for years, who processed a refund I was due, but only after I spotted the error myself. The money came back, but a small dent remained in my confidence. That is how trust works. It notices the moments when you could have acted and did not.

    Why young firms get this wrong

    In the early years, founders rarely have the experience or the tools to handle harassment complaints well. The instinct is often to downplay, to manage, to protect the team you worked so hard to build. That instinct, however understandable, is exactly how small problems become large ones.

    There is also a second trap. In a small firm, the accused is often someone the founder personally recruited, worked alongside in the trenches, and considers a friend. Objectivity becomes genuinely hard. This is precisely why the complaint process cannot rest on the founder’s judgement alone. It needs to sit with people who can act without that emotional entanglement, follow a defined process, and report findings honestly.

    I learnt a related lesson early in my publishing family’s business, which I write about in a chapter on guiding people through change. When I introduced new software into my father’s firm, the resistance came not from the technology but from people’s fear of the unfamiliar. I eventually realised that the breakthrough came through an unexpected internal champion, the senior-most manager, whose endorsement made the change acceptable to everyone else. The same principle applies here. A workplace safety policy on paper changes nothing. A respected senior person inside the firm, visibly committed to taking complaints seriously, changes everything.

    The founder sets the ceiling

    There is something I often remind myself. A team will not take safety, fairness, or ethics more seriously than the founder visibly does. If the founder rolls their eyes at compliance training, so will everyone else. If the founder treats an uncomfortable complaint as an inconvenience to be managed, the team learns that such complaints are inconvenient to raise.

    The same applies to safety. A founder who signals, through action, that no revenue target and no star performer is more important than the dignity of the team builds a firm that people stay in and recommend. A founder who signals the opposite, often without realising it, builds something more fragile than they know.

    What to do before you need to

    The good news is that today, unlike when I started, there are serious professionals who help firms build the right systems. My friend Viji Hari has spent years doing this work, and her book Behind Closed Cubicles is one I often recommend to founders. Industry bodies and local business chambers also run regular workplace conduct and anti-harassment programs for founders and their teams. Please attend them. Send your HR lead. Send yourself.

    A few practical steps any founder can take this quarter:

    I am not the person to give you a checklist for building these systems. That work belongs to specialists who spend their careers on it, and the rules vary by country, by industry, and by the size of your firm. What I can offer is a few questions worth asking yourself this quarter, the same questions I have seen good founders ask when I mentor them.

    Does every person in your firm know, without hesitation, whom they would go to if something happened?

    Is that person someone with enough independence to act, or someone who reports to the alleged offender?

    When was the last time you, as the founder, personally sat in a workplace conduct training session rather than sending a calendar decline?

    If a complaint landed on your desk tomorrow, do you have a clear process to follow, or would you be figuring it out under pressure?

    None of these questions has a universal answer. But sitting with them honestly and then finding people who do have expertise in your context is itself the first step. Speak to a lawyer who specialises in employment matters in your country. Speak to HR professionals who have built these systems in firms larger than yours. Speak to founders who have quietly handled difficult situations well; they exist, and most are willing to share if you ask privately.

    The long view

    When I look at firms that have lasted across generations, and I have studied many of them, including my own family’s seventy-year-old publishing business, one thing stands out. They treated the quiet, unglamorous work of keeping faith with their people as seriously as the glamorous work of winning new customers. They paid salaries on time, kept a clean record with vendors and governments, and when something went wrong, they fixed it without being asked twice.

    The firms that will earn the trust of the next generation of employees, customers, and investors are the ones that take workplace safety as seriously as product quality. In a world where every incident travels on social media within minutes, there is no quiet way to handle these things later. Only an honest way to prevent them now.

    Do not wait for a problem to force the learning.

    What are you doing this quarter to make sure someone in your firm can speak up without fear?

  • How forecasts age in an AI moment?

    How forecasts age in an AI moment?

    Every quarter, someone in tech quietly walks back a confident prediction they made not long ago.

    In January 2026, Forrester published research showing that over half of all AI-attributed layoffs are likely to be reversed, because companies cut headcount based on AI promises that the technology was not yet ready to keep. The same month, Gartner forecast that generative AI in customer service would cost more than offshore human agents by 2030, prompting a pointed rebuttal from industry practitioners who argued Gartner was describing a vendor pricing failure, not an AI failure.

    The retractions are usually healthier than the original predictions. They just get a fraction of the attention.

    In October 2021, a year before ChatGPT, I delivered a talk called “The Future of Software Developers by 2040” to a developer conference. Looking back now is not about whether I got things right. The more useful question is which assumptions survived contact with a shock nobody saw coming.

    A few stood up reasonably well. The talk argued the (Indian) IT hiring boom would not last. TCS, Infosys, Wipro and HCL were on record quarters then, with attrition above 20 per cent. The direction was right, though the mechanism I imagined, gradual automation and growth of the global capability centres, turned out to be the smaller story. The larger one was a foundation AI model that could write working code from a plain English comment.

    One slide, titled “Going to be upside-down,” argued that the work mix in software would invert: more time on business understanding and customer needs, less on writing and testing code. That was framed as a gradual shift toward 2040. It is largely the reality in 2026 itself.

    The line that aged the worst was the one I closed with: let machines be machines, and let humans be human. It earned a warm nod in the room. Five years on, it has the ring of a sentence printed over a stock photo of a sunrise. Tidy aphorisms assume the boundary they describe will hold still. This one has not.

    Nobody in this industry predicts precisely. The people who navigate these shifts well are not the ones with the sharpest forecasts. They are the ones who revise quickly when the ground moves, before it costs them too much.

    There is a Chinese proverb associated with Deng Xiaoping: crossing the river by feeling the stones. You do not map the riverbed in advance. You step, test your footing, adjust, and step again. The destination matters. But the only honest method is one stone at a time.

    The next five years will not reward people who pick a single thesis and hold it. They will reward those who can hold a direction loosely, gather evidence weekly, and change their mind without ego when the facts demand it.

    The cost of a wrong forecast held too long is now measured in quarters, not years. The cost of revising one in public is mostly ego. And ego is the cheapest thing to spend.

    The forecasts that age worst are usually the ones that sound the wisest at the time.

  • Nobody owns this and nobody will admit it

    Nobody owns this and nobody will admit it

    Every organisation has a WDNW system. Most have several.

    A process that runs quietly in the background, consuming time and money, that nobody questions because it has never visibly broken. The moment someone asks what it actually does, the honest answer is usually some version of “we are not entirely sure, but we are afraid to touch it.”

    Every organisation has a WDNW system. Most have several.

    Startups are not immune to this. Technical debt accumulates faster than most founders realise. A workflow stitched together in year one, a third-party integration nobody remembers choosing, a report generated every Monday that nobody reads but everyone continues to produce. These are not dramatic failures. They are quiet drains.

    This is one area where I think AI genuinely earns the word superpower. Not in building new things, but in helping us see what already exists with fresh eyes. AI tools can now map workflows, surface redundancies, flag processes with unclear ownership, and help teams ask the question they were too busy or too hesitant to ask themselves: why are we still doing this?

    The best technical debt reviews are not always led by consultants or new CTOs brought in to shake things up. Sometimes all it takes is a founder sitting down with an AI tool and asking honest questions about their own workflows. No agenda, no politics, just a clear audit of what exists and why.

    And you do not need a fancy specialised auditing platform that costs a fortune to get started. Tools like Claude Code or GitHub Copilot are more than capable of helping you take those first steps. Start small, pick one corner of your codebase or one workflow, and go from there. Do not try to churn the ocean in one go. With frontier AI tools, there are no real experts yet. Everyone is figuring their way through. The important thing is to keep moving, keep experimenting, and keep learning, carefully and with guardrails in place.

    How many WDNW systems are hiding in your organisation right now? If you are not sure where to begin, I am happy to think it through with you.

  • AI Is Here. Now What?

    AI Is Here. Now What?

    The Ultimate Playbook for the Resilient IT Services CEO

    If you are running a mid-sized IT services firm today, you know the ground is shifting. You are part of a resilient industry that has survived COVID, remote work transitions, resource crunches, double-digit attrition, and geopolitical upheavals. Now, just as you are catching your breath, comes the most unsettling wave of all: artificial intelligence.

    In the next 6 to 12 months, your job is not to build “rigid forecasts”. Your job is to filter the hype, preserve your profits, and refuse to bet your company on speculative projects. The real churn is coming in the mid-term—contracts will be renegotiated, and maintenance work will shrink. Only the firms that adapt their business models now will come out stronger.

    The Ultimate Playbook the Resilient IT Services CEO
    The Ultimate Playbook for the Resilient IT Services CEO

    1. Operations & Financial Discipline: The Next 6–12 Months

    Winning this transition isn’t about playing defence; it’s about ensuring you have the financial fuel to cannibalise your own revenue before a competitor—or an AI agent—does it for you. That level of aggression requires ruthless efficiency today, not chasing “future riches” on borrowed time or capital.

    • Elevate Beyond Code Gen: Your engineers are already using AI to write code. You must now require the use of AI agents to massively increase testing coverage, stress testing, and deployment speeds. This delivers a significantly better, more resilient product to the client at zero additional infrastructure cost.
    • Strict Financial Preservation: Prioritise profits and save your cash reserves. Do not take on debt or run at a loss for speculative AI projects in the hopes of future riches.
    • Zero Free Work: Refuse to do unpaid AI Proof of Concepts (PoCs). Do not undercut your pricing just because a requirement document is filled with AI buzzwords. Any effort your team puts in must be compensated fairly.
    • Cap Experimentation: Provide the latest AI tools, but mandate that usage is reviewed. Ensure learning does not consume more than a single-digit percentage of your team’s time so you can deliver current projects without extra manpower.
    • Be Brutal with Right-Sizing: Review your team size and expenses every month. There is no virtue in holding a large bench. Scrutinise every timesheet to see what roles can be automated by AI agents.

    2. Strategy: Talent & Client Management

    Stop asking “How do we use AI?” and start defining what moat you actually provide to your clients.

    • Filter Hype and Temper Marketing: Take predictions from frontier AI labs with a grain of salt; much of it is self-fulfilling prophecies. Do not let your marketing team claim expertise in every AI technology (LLMs, MCPs, agents), as clients will see there is no real differentiation. Project your true niche experience and affordable rates.
    • Over-Communicate: Call every single client weekly. Listen carefully to their specific fears and successes with AI. When asked, be transparent about your successes and failures with AI. Here is a sample template for these Weekly Client AI Discussion calls to keep them focused. It moves the conversation away from buzzwords and toward the “moat” you are offering. (See the attached infographic)
    • No Mandates or Panic Firings: Avoid top-down AI mandates—they do not work. Likewise, do not let go of good talent simply because you see knee-jerk trend reductions in the rest of the industry.
    • Hire AI-Native Engineers: Avoid paying massive premiums for lateral AI hires, save for a couple of key, hands-on experts. Instead, hire affordable, AI-native, fresh engineering graduates who are already exposed to these workflows. Value honesty and the ability to learn fast over pre-existing knowledge.
    Template for the Weekly Client AI Discussion
    Template for the Weekly Client AI Discussion

    3. The Pivot: Contracts & Service Evolution (1–3 Years)

    If you stay on Time & Materials (T&M) contracts, you are handing all your productivity gains back to the client.

    • Shift to Outcome-Based Pricing: You must urgently move your Time & Materials (T&M) contracts to outcome-based pricing. This is the only way for clients to see the financial benefits of your internal AI productivity gains immediately.
    • Cannibalise Your Own Revenue: Approach your champion clients now with radical cost-saving ideas—up to 50% or more. Identify legacy apps and systems that AI can automate and/or eliminate. If you do not do this, your competitors or an AI agent will do it for them. However, never agree to work at a loss for maintaining current run rates.
    • Pivot to Orchestration, Not Products: Resist the urge to divert R&D resources into building proprietary AI SaaS products or enterprise apps just because you can. Enterprise CXOs are shifting toward “build” rather than “buy,” and you lack the deep pockets required for marketing distribution. Instead, focus on integrating and orchestrating custom bots and agents for enterprises, managing change processes, and providing industry context.
    • Capitalise on Sovereign AI: Governments are treating AI as a national security issue. Use this shift to help enterprises implement localised, sovereign AI infrastructure and diffusion.

    4. Long-Term Strategic Moves (3+ Years)

    • Strategic Maturity: If your firm is profitable and holds domain expertise, be open to external investment or acquisition. Larger businesses will be looking to strengthen their portfolios during this transition, and being open to this is a sign of strategic maturity, not weakness.
    • New Industry Openings: Generative AI is going to lower barriers to entry in traditionally closed-off industries like film and media. Be ready to walk through those doors and serve those industries in entirely new ways when the disruption creates opportunities.

    Build Resilience, Not Forecasts

    From where we stand today, it is impossible to see the distant future clearly, as transformative technologies take time to diffuse within enterprises. AI itself will keep evolving well beyond current (large language) models, gaining capabilities that are hard to imagine right now.

    Because timing and execution determine who survives a transition, it is far wiser to build organisational resilience than to rely on rigid forecasts. If you navigate this transition with discipline, we will look back at this period in 2030 and be proud of a job well done.

  • Why Unhappy Leave Can Break a Startup

    Why Unhappy Leave Can Break a Startup

    The internet has a long memory, but a very selective one.

    Old news resurfaces without warning. Context quietly drops off. A half-forgotten announcement suddenly becomes today’s debate. That is exactly what has happened over the last few days with a decision taken in China in 2024.

    Many are now discussing something being called Unhappy Leave.

    On the surface, the idea is simple.

    Employees are given ten additional days of leave every year. These days can be taken purely at their own discretion. No medical certificate. No justification. Just the employee deciding they are unhappy, stressed, or mentally exhausted. The important detail is this. If a manager denies this leave, it is treated as a policy violation.

    In a country known for long working hours and intense workplace pressure, this sounded radical. Predictably, the internet amplified it. It praised it. Compared it. And in some cases, reduced it to a slogan.

    The policy was announced by Yu Donglai, the founder of Pang Dong Lai, a Chinese retail chain admired for its employee-first culture. The announcement was made in 2024 and quietly implemented. It went viral much later. That delay itself is telling. Good ideas do not always travel fast. Sometimes they wait for the right moment, or the right outrage cycle.

    At first glance, this feels like a bold and humane move. And it is. There is no denying that.

    It also reminds many people of earlier experiments in employee well-being, especially in the West.

    One obvious comparison I will make is with Tony Hsieh of Zappos. Long before workplace culture became fashionable, he spoke about trust, happiness, and unconventional benefits. At the time, many dismissed it as idealistic. History was kinder. Zappos built loyalty, reduced attrition, and created a culture that people still reference years later.

    So yes, Unhappy Leave is innovative. It is empathetic. It signals trust.

    But this is where founders and business leaders need to pause.

    Large organisations have buffers. They have HR teams, policy frameworks, and workforce planning models. They can simulate impact before announcing a benefit. They can course-correct if something breaks.

    Founder-led companies and startups do not have that luxury.

    This is where good intent can quietly turn into long-term damage.

    When a policy like this is announced without thorough internal thought, a few things happen quickly. Project timelines slip because key people are unavailable at critical moments. Managers hesitate to question leave decisions even when delivery risk is high. Teams compensate informally for absent colleagues. Resentment builds.

    The irony is this. A benefit meant to improve morale can end up eroding it.

    Missed delivery commitments create pressure elsewhere. Customers feel the impact first. Revenue follows. Leadership starts firefighting. Slowly, the same policy that was announced with warmth becomes a source of quiet frustration.

    And when leaders later attempt to dilute or roll back the policy, trust takes a direct hit.

    This is especially risky with Gen Z employees.

    Gen Z values authenticity more than perks. They sense inconsistency very quickly. If a benefit is announced and then poorly executed, the damage is not temporary. It creates a belief that leadership announcements cannot be taken at face value. That kind of trust loss is extremely hard to repair.

    In my experience, and this is my opinion, most employee benefit schemes fail not because of bad intent, but because of shallow implementation.

    Every founder who grows into a CEO learns this lesson sooner or later.

    Policies are promises. The moment you announce them, they become part of your culture. Culture cannot be paused, renegotiated, or quietly edited without consequences.

    Founder-led organisations should address key issues before announcing new ideas. Start with open conversations about challenges like fairness and peak periods, run small pilots to learn, and only expand once ready. Consistency, more than generosity, builds trust.

    This is also where founders benefit from an external sounding board. Someone who has seen these cycles play out across companies and stages, and can help leaders think through second-order effects before ideas turn into irreversible promises. That role, walking alongside founders as they translate intent into execution, is exactly why I do what I do as a Founder Catalyst.

    Unhappy Leave works at Pang Dong Lai because it sits inside a much larger system of values, planning, and discipline. It is not a standalone perk. It is a visible expression of an already mature culture.

    Stripped of that context, it becomes a slogan.

    The internet loves slogans. Organisations live with consequences.

    Reflection:  Every policy announcement feels reversible in the moment. Very few actually are. Founders do not just introduce benefits. They set expectations that quietly shape culture long after the applause fades.

    If you prefer to receive more of these insights directly, subscribe to my Founder Catalyst Digest, where I share practical lessons on leadership, AI, and building companies that last.

    Read my book “The Founder Catalyst” for more lived lessons: The Founder Catalyst on Amazon

  • A Lesson for Founders Who’ve Been Cheated

    A Lesson for Founders Who’ve Been Cheated

    I watched a short video of Frank Abagnale recently. It stayed with me longer than I expected. 

    (Video attached at the end)

    Frank Abagnale is best known as the real-life inspiration behind the movie “Catch Me If You Can“, played by Leonardo DiCaprio. But this two-minute clip has nothing to do with glamour or clever cons. It is about a quiet, painful reality that many founders face but rarely talk about.

    Every business owner, at some point, encounters theft or fraud from within.

    An employee lies. Someone manipulates accounts. Company assets quietly disappear.

    In a large organisation, this is absorbed by process. HR steps in. Finance handles it. Lawyers do their work. The founder signs papers and moves on.

    In a startup, it is very different.

    A small theft can be fatal. Cash flow is thin. Margins are fragile. More than the money, the emotional damage is deep. Almost every founder I have met who has gone through this carries that pain for years. The feeling of being cheated. The sense of having trusted the wrong person. The embarrassment of feeling foolish. This does not fade easily.

    In the video, Frank Abagnale talks about a pattern he sees almost every day. Small business owners write to him after an employee steals money. They do everything “right”. Police complaint. Court case. Trial. Depositions. And after years, they get nothing back. No restitution. Just exhaustion.

    His advice, in the US context, is pragmatic and slightly ruthless. He suggests using the tax system. Since stolen money is considered taxable income in the US, he advises filing a 1099 with the IRS. This gives the victim a tax write-off and puts the burden on the offender through the tax authorities, who have far more power than a criminal court in recovering money.

    This specific solution does not apply to most of the world. Certainly not in India. So I am not suggesting founders copy this approach.

    What struck me was the lesson beneath the advice.

    To me, the real message of the video is not about the IRS. It is about how a founder should respond after being cheated.

    • First, acknowledge the fraud honestly. No denial. No rationalising. No self-blame. Accept that it happened.
    • Second, fix the system, not the past. Put processes in place so it does not happen again. Separation of duties. Basic controls. Regular reviews. This is unglamorous work, but essential.
    • Third, stop obsessing over punishment. This is the hardest part. Wanting justice is natural. Wanting revenge is human. But chasing punishment rarely brings the money back. Worse, it steals your most valuable asset. Your time and mental energy. Time that should be spent rebuilding the business and creating future income.
    • Fourth, be brutally realistic and move on. Some losses will never be recovered. Accepting this is not weakness. It is survival.

    Frank Abagnale delivers this lesson calmly, without drama. That is what makes it powerful.

    So, thank you Mr Frank Abagnale, for a lesson many founders learn only after it hurts deeply.

    Sometimes, wisdom is not about winning. It is about knowing when to stop bleeding and start walking forward again.

    This strategy is from Frank Abagnale’s 2013 talk for Nevada State Bank. While US tax law does treat stolen funds as taxable income, using it as a recovery tool is a legal “gray area” and is shared here as a strategic insight, not formal advice. Consult a professional before application.

    Here’s the video:

    Short summary of the video transcript:

    Frank Abagnale explains that many small business owners write to him after employees steal money. They pursue police cases and court trials, but usually recover nothing. He says that in the US, stolen money is taxable income, so filing a 1099 with the IRS allows the victim to claim a tax deduction and shifts enforcement to the tax authorities, who have stronger powers than criminal courts. He believes the threat of tax consequences is often more effective than sending someone to jail.

    If you prefer to receive more of these insights directly, subscribe to my Founder Catalyst Digest, where I share practical lessons on leadership, AI, and building companies that last.