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The tech world is rife with stories of incumbents launching internal challengers to combat agile startups. One of the more telling examples is Siebel’s attempt to fend off Salesforce by rolling out CRMOnDemand.What happened? Instead of solidifying their position, Siebel created chaos. With two teams competing for the same customers, confusion reigned. One team could win, but everyone lost in clarity. Sales strategies became muddled; customers faced mixed messages, and even positioning suffered.As companies like ServiceNow look to roll out their own AI-native solutions (Flow), this history repeats itself. Two teams fighting over the same client just leads to chaos: a lesson worth heeding.Instead of launching internal competitors, incumbents might consider a hard pivot or even acquisition. Just look at what Adobe did and what Salesforce is doing now.The takeaway here? Don’t let internal rivalry cloud your strategy. Embrace clarity instead of chaos. What's your take on ServiceNow's launch of Flow?

I’ve been using Wispr Flow for the last six months and it has become one of those products that quietly slips into your daily workflow. After a shoulder injury from playing tennis, typing for long periods became uncomfortable, so dictation wasn’t just a productivity hack for me—it genuinely made working easier. Ironically, I stopped using it last week. [The show, Rumble with Sinha is co-hosted in partnership with Rumble, a platform that enables one to do a podcast with their AI co-host - after all, solo talks are boring!] Not because I found a better product, but because I ended up building my own Mac client. It does almost everything I need, and that got me thinking about something much bigger than my own setup. I don’t think Wispr Flow has a product problem. I think it has a business model problem. The underlying technology has become a commodity. Whether you use Whisper.cpp, Parakeet, Faster-Whisper or any of the newer open-source models, the gap between a polished commercial product and a free alternative has narrowed dramatically. That’s not a technology problem. That’s a pricing problem. I suspect there are still plenty of people happily paying for Wispr Flow simply because they don’t know these alternatives exist. But information arbitrage doesn’t last forever. Developers are already discovering them, building their own clients, and sharing them. Over the next year or two, I expect this to become mainstream. If that happens, I don’t think Wispr Flow should fight open source. It should lean into it. In fact, I’d seriously consider making the consumer product free. That sounds counterintuitive because AI products cost money to run. But I don’t think consumers are the business anyway. I think enterprises are. The Slack analogy comes to mind. Slack didn’t start by selling to CIOs. Individuals and small teams adopted it first because it was useful. Once enough employees were using it, companies had to buy it. Wispr Flow could follow exactly the same playbook. Let individuals get addicted to voice-first computing and let them bring that habit into their workplace. That’s where security, compliance, centralized administration and enterprise workflows become valuable enough for companies to pay. There’s another asset that Wispr Flow has which people don’t talk about enough: voice data. Before everyone gets upset, I’m not suggesting the company secretly sells user conversations. That would destroy trust overnight. But what if users had a choice? Pay for complete privacy, or use the product for free and explicitly opt in to contributing anonymized speech data for model training. That’s a much more honest trade-off. The reason I think this is particularly interesting is because of India. Wispr Flow has publicly said that India is one of its largest markets. At the same time, every major speech AI company wants more high-quality multilingual voice data. Today, companies are literally paying people to record speech in studios because good datasets are hard to find. Wispr Flow already sits on top of exactly that kind of usage. Every day, thousands of people dictate naturally instead of reading scripted sentences into a microphone. With explicit consent, that becomes a valuable asset. Maybe data licensing never becomes the biggest revenue stream. Maybe it simply offsets the cost of serving free users. Either way, I think it’s a more interesting direction than trying to convince consumers to keep paying a monthly subscription for dictation. The enterprise biz doesn’t change. In fact, it probably becomes stronger. No company wants its sales calls, customer emails or internal strategy documents entering public training datasets. They’ll continue paying for privacy, compliance and contractual guarantees. That’s where the money is. Consumers and enterprises don’t need the same pricing model. What's you're take?

Everyone is talking about Kunal Shah becoming CEO of WhatsApp. I think they’re looking at the wrong story. The bigger story is that Meta suddenly needs WhatsApp to work. For years, WhatsApp has been one of the most successful products in the world. But it has also been one of Meta’s biggest unfinished businesses. Meta paid $19 billion for WhatsApp more than a decade ago. Yet WhatsApp still contributes a tiny fraction (less than 2%) of Meta’s revenue. Now look at what’s happening elsewhere. Meta is spending aggressively on AI. Employee morale is reportedly under pressure. The company is reorganizing teams, moving talent around, and throwing enormous resources at finding its next growth engine (Llama has failed badly). Which makes WhatsApp more important than ever. And nowhere is that challenge more visible than India. India is WhatsApp’s biggest market by users. India is one of the biggest WhatsApp Business markets. But the monetization numbers tell a very different story. India accounts for a massive share of WhatsApp usage while contributing a disproportionately small share of revenue. That is the paradox Meta has failed to solve. For years, people predicted that WhatsApp Payments would dominate Indian fintech. It didn’t happen. Not because the market wasn’t large enough. Not because Indians weren’t willing to adopt digital payments. Other fintech companies proved both assumptions wrong. The real issue was execution and focus. That’s why I don’t see Kunal Shah’s appointment primarily as a symbolic moment for Indian leadership. I see it as a business decision. If Meta wants WhatsApp to become a meaningful business, it has to crack India. And if it can crack India, it may finally discover the monetization playbook for the rest of the world. The question is no longer whether WhatsApp product can scale. It already has. The question is whether Meta can finally figure out what business WhatsApp is actually in.

Most companies building marketplaces struggle with one simple truth: demand and supply need to grow together, but early hacks often go unnoticed or unscalable. Amit, CEO and co-founder of NoBroker, reveals the counterintuitive lessons learned from disrupting the $50 billion real estate industry—lessons that could redefine how you think about scaling any marketplace. From avoiding the trap of expanding too fast in unrelated cities to mastering trust through data and customer insights, this episode distills 12 years of relentless experimentation into actionable takeaways.You'll discover how tiny hacks—like leveraging simple street ads or college student outreach—created explosive growth in early days, and why focusing on building depth in fewer markets beats superficial expansion. Amit shares how insisting on transparency and honest data—like the rent-o-meter—builds trust that scales, even when it costs short-term revenue. We break down the importance of charging early, navigating incentives, and adapting to changing customer behaviors, especially among Gen Z and high-value decision makers.======Chapter 1: Introduction to NoBrokerAmit Kumar, CEO of NoBroker, shares the vision behind creating a platform that eliminates the need for traditional brokers in real estate. Discover the simple yet powerful idea that sparked a revolution in the industry.Chapter 2: The First 100 DaysExplore the early challenges and decisions that shaped NoBroker's journey. Learn about the initial assumptions, the surprising realization about mobile apps, and the unique consumer behaviors that influenced their strategy.Chapter 3: Marketplace DynamicsAmit discusses the hacks and strategies used to balance supply and demand in a marketplace. From leveraging street ads to crowdsourcing real estate data, discover the innovative tactics that fueled growth.Chapter 4: Expansion vs. DepthWhy did NoBroker choose depth over rapid expansion? This chapter goes into the counterintuitive strategy of focusing on fewer markets to build a stronger presence and customer satisfaction.Chapter 5: Navigating Incentives and EconomicsUnderstand the complexities of charging customers in a trust-deficit economy. Amit reveals the challenges of introducing paid plans and the insights gained from customer feedback.Chapter 6: Behavioral Insights and AdaptationExplore the surprising consumer behaviors that NoBroker encountered and how they adapted their services to meet diverse customer needs. Learn about the importance of listening to customer feedback and evolving with changing expectations.Chapter 7: Building Trust and TransparencyDiscover how NoBroker uses technology to provide accurate information and build trust with consumers. Learn about the rent-o-meter and other tools that empower users to make informed decisions.Chapter 8: Conclusion and Future OutlookReflect on the journey and insights shared by Amit Kumar. Understand the ongoing commitment to customer satisfaction and the future direction of NoBroker in the evolving real estate landscape.======If you're an entrepreneur or investor eyeing the marketplace space, missing these insights could mean sacrificing long-term trust and growth. Conversely, embracing customer-centric experimentation, frugal operations, and authentic credibility opens the door to dominant market share and loyal communities.Ideal for founders, product teams, and growth hackers, this episode proves that agility and customer obsession—paired with bold data-driven honesty—are the real keys to marketplace success. Ready to challenge your assumptions and learn what it really takes to thrive in a fast-changing consumer landscape? Hit play now.

You have a "God-level" product. Your pricing is perfect. Your UI is beautiful. So why aren't customers buying? In this video, Ashish breaks down the brutal reality of why great SaaS products fail—and it has nothing to do with your features.Most founders make the mistake of trying to change a company's behavior. In SaaS, if your customer doesn't already have an existing workflow for the problem you’re solving, you aren’t just selling a tool—you’re trying to change a culture. That is a losing battle for early-stage startups.What You’ll Learn- Why "God-level" products still get rejected by the mid-market.- The massive difference between Consumer growth loops and Enterprise reality.

Sam Altman frequently warns that most founders fail not because they don't work hard, but because they choose the "enjoyable path" over the "ambitious path." i.e. fake work.They spend 12 hours a day on Slack, tweaking UI colors, and attending networking events—tasks that feel productive but move the needle exactly zero inches.In this video, I break down a simple 2-circle Venn diagram to help you audit your life.What we cover: Why Sam Altman dreads the first and last hour of his day.The "Slack Trap": Why internal communication feels like progress (but isn't).The Danger Zone: Premature scaling and "Political Theater."The 24-Hour Rule: How to filter your tasks for Real Work.The Bottom Line: We don't fail because we are lazy. We fail because we choose comfort over truth.I’ve been guilty of this myself—polishing a landing page for hours just to avoid making a sales call. What’s one 'Fake Work' task you’re going to cut from your schedule tomorrow? Let’s be honest in the comments. 👇

90% of consumer AI ideas look sexy but crash hard — most end up in the “tar pit” with zero real traction or revenue uplift.  For e.g. that new ‘productivity app’, that ‘event discovery platform’ and an all-time fav, ‘friend finder’ :) In this video, I break down why so many consumer AI startups fail and share a realistic 2×2 framework that separates doomed ideas from winners. X-axis: Existing Behavior vs New Behavior Y-axis: ARPU * AI, i.e. Low ARPU uplift vs High ARPU uplift from AI (if AI does’t help you increase the TAM / revenue..then what’s the point). We cover real examples: Why short-news apps or generic AI tools built in hours almost always die (existing behavior + low ARPU) The massive opportunity in AI astrology (existing behavior + high ARPU via subscriptions) Why AI companions (new behavior + low initial ARPU) are VC catnip right now And the mysterious high-ARPU new-behavior quadrant — what could live there? If you’re building in consumer AI, raising funds, or just trying to spot real opportunities instead of hype, this framework will help you avoid wasting time and money.

A rerun of my conversation with Shane Parrish of Farnam street. The conversation is a reminder about what matters in one's life - did you focus on the outcome or..your ego? I made a lot of tough decisions this year (thanks to a near death experience) and the mental model of outcome vs. ego kept coming to me - hence this rerun.

How to build successful startups. Watch this 30 mins MBA (well, almost) with K Ganesh - serial entrepreneur and investors behind successful startups like BigBasket, Portea Medical, Bluestone, HomeLane, Freshmenu.In this episode (the last one in the series), Ganesh and Ashish discuss several topics ranging from the next big opportunities in India to traits of a successful Indian company.1. Why vanity metric work (and don't)2. The top 5 sectors in India3. The next big what , i.e. big opportunities in India4. Are Kirana scores under threat?5. Follow the money: The #1 principle K Ganesh has when it comes to evaluating startup ideasK Ganesh has also published his book bringing together the best of his learnings - Mastering Disruptions : https://amzn.to/3FgZgDOUnPluggd with Sinha podcast is a new format bringing together the very best of India's high-growth businesses across a range of industries - done in a very candid, unpluggd style!

Akshayakalpa is a weird business (weird in a good way) - it is one of the largest soil management companies in India - but ended up selling milk to create a sustainable business for its stakeholders (primarily farmers). And in the process, created a 400 cr+ (~$50mn) revenue business - accidentally! My next guest in #UnPluggdWithSinha podcast is Shashi Kumar, Cofounder and CEO of Akshayakalpa - India’s first certified organic dairy enterprise bringing together a balance of practices based in science and the traditional heritage of dairy farming. Topics we discussed: How Akshayakalpa’s business model is a gamechanger (It is among the top soil management company in India, ended up selling milk as it offers a compelling value prop to farmers - faster revenue cycle being the most important one). Shashi’s startup journey - he worked with Wipro, was in US. So why come back to India and go back to farming - something his parents wanted him to never take up (can you believe that his father did not speak to him for 8 years after he decided to take up farming as a career).- Why India being one of the largest producers of milk doesn’t stand a chance in global diaspora?- Why most agritech startups trying to disrupt middle-men fail.- What’s the future of food? Answer: We are in a serious trouble As a consumer - what is it that you need to understand about food ecosystem? Is your food producer making enough money? The UnPluggd with sinha conversations bring you the very best of India’s startup ecosystem (minus the hype) in an informal setup.Wanna suggest guests for the show? Use this form: https://forms.gle/J1GfdGifcVgB7gA87—

He is one of the OGs of new age retail businesses.  Raised funding - as expected great expansion plans et al.  The money didn't hit the bank account (some % did)  Almost lost his startup to investors - somehow got it back (had to take a loan)  Activated the 'sane' mode - survived, scaled up.  Meet my next guest in #UnPluggdWithSinha show - Manish Sharma, cofounder and CEO of Printo, a leading printing business in India. UnPluggd with Sinha podcast brings you unfiltered, candid conversations with India's leading entrepreneurs, authors and business leaders.  In this episode, we discuss Manish’s lessons building Printo Raising money, the (evil) power of liquidation preference clause most founders often ignore Building one of the early D2C businesses in India His advice to founders out there Why founders should have a life beyond their startups - Manish is into cycling, was intensely involved with AAP political party (Bangalore chapter), does theatres. (episode language: Hinglish)

Balkrishna Birla has started 2 of India's hottest startups - ZopNow and Asklaila. In this conversation with Ashish Sinha, he shares lessons learned building these companies - his 'Steve Jobs' moment, where he was fired from his startup to his candid take on startups - I.e startups are not sexy, there are many ways to make good money (and handling #personalfinance is the first step). Apart from being CTO of an AI startup, he also conducts workshops on #personalfinance for young working professionals. UnPluggd with Sinha podcast is aimed at bringing the very best of India's business and tech leaders - for candid, unfiltered conversations. use this form to suggest show guests / share feedback : https://forms.gle/nhfYREbrZ5d7TM5L6

Deepak Shenoy is founder and CEO of Capital Mind, a pioneering financial analytics and investment research firm. Deepak and I talk about his first startup, the early days of building CapitalMind, being rejected by VCs (heck ! this is just a blog!!!!) to building a solid business…and lessons learned scaling CapitalMind. building capitalmind from a blog to leading financial research company Deepak’s early days of running tech startups and lessons learned Deepak Shenoy’s relationship with money His take on the power of compounding His advice to new investors ------------- Contact us for feedback / speaker suggestions / exploring partnerships using this form: https://forms.gle/Q6hZdXaYfeiQqR2x8 This show is brought in partnership with VidRow team.

Introducing : UnPluggd with Sinha - a new show, and a refreshingly new format (none of those standard studio setups)! UnPluggd with sinha is the show where real, raw, and refreshingly candid conversations happen on the move - no made-up studios! Because the best ideas don’t need a stage – just a car, a drive, and a great guest. We are breaking away from the studio format and creating a unique experience that - is out there in the real world (no *made-up studios*) - fosters great candid conversation with guests *(can’t get real than this)* - Makes guests open up and discuss a variety of topics The first episode goes live this Wednesday (Jan 29th).

In this conversation, Ashish Sinha interviews Arvind Narayanan, professor of computer science at Princeton University and co-author of the book AI Snake Oil (https://amzn.to/402Ayiw), discussing the impact of AI on various sectors, the differences between generative and predictive AI, the challenges of AI agents, and the future of AI technology. We explore the importance of human-AI collaboration, the role of reasoning in AI, and the need for better evaluation criteria to build trust in AI systems. Narayanan emphasizes the necessity for technical breadth over mastery in the evolving job market and shares practical applications of AI in education and research. Unique Quotes from the Conversation On Predicting Creative Success "The success of cultural products relies on chance elements that cannot be predicted in advance." This highlights the inherent unpredictability of creative ventures, whether driven by AI or humans. -- On Generative AI in Programming "It’s not that AI writes better code, but it takes so much of the drudgery and boring parts out of it." A reminder that AI is a tool to enhance, not replace, human creativity in programming. -- On Predictive AI’s Ethical Concerns "These tools are only slightly better than random at making really consequential decisions about people." A critique of the overreliance on AI in life-altering decisions like hiring or criminal justice. -- On the Capability-Reliability Gap "The capability-reliability gap means these systems are not reliable right now." An acknowledgment of AI’s limitations, emphasizing the need for better testing and accountability. -- On Preparing for the Future of Work "Technical mastery is less valuable than having technical skills combined with a breadth of skills." Advice for future professionals to combine technical expertise with adaptability and interdisciplinary thinking. -- Takeaways The unpredictability of success in creative products is a key theme. Generative AI is widely recognized, but predictive AI poses ethical challenges. AI agents must be more than just wrappers around models. Benchmarking AI in complex environments is a significant challenge. The capability reliability gap highlights the unreliability of current AI systems. Human-AI collaboration is crucial for effective AI deployment. Inference scaling is a promising area for improving AI performance. Trust in AI is at risk due to rapid deployment without proper evaluation. Future engineers should focus on technical breadth and adaptability.