Understanding the Tech Startup Ecosystem

A VC friend just told me: "I see AI startups hitting $400K in 4 months…and I still pass." Wait, what? 🤯 A few years ago, that kind of early traction would’ve made fundraising easy. But today? VCs are hesitating. Because in today’s world, fast revenue is easy. Sustainable revenue? That’s another story. 🔹 Copycats are coming for you faster than literal cats to a bowl of milk. The barriers to building AI products have never been lower (bc of AI coding, plug and play software, open source, etc). If you hit traction, expect a dozen competitors to spin up in months. 🔹 Customer loyalty is at an all-time low. Switching costs are practically zero. Onboarding is seamless, so users have no reason to stay if something shinier comes along. And if they're early adopters, they probably feel drawn to trying the new thing even if it's just marginally better. So how do you defend your growth when AI makes everything easier to build—but harder to retain? 1. Data as a moat: Look at Gong ($7.5B valuation.) They don’t just analyze sales calls, they own your sales conversation data....why are you closing deals and who is doing it the best, which means insights get better over time. Every interaction makes their product stronger, creating a sunk cost that companies don't want to lose and that competitors can’t easily replicate. 2. Community as a moat: Then we've got Character.AI, which reached a $2.5B valuation in 3 short years. They've got 20M+ users co-creating AI characters. The more people engage, the more irreplaceable the ecosystem becomes. A competitor can copy their tech, but they can’t clone the network effects of millions of engaged users. 3. Be the infrastructure: If you're the bedrock under a house, no one is trying really hard to extract you. Veeam Software is a prime example of this. They provide backup, disaster recovery and modern data protection software. They also had the #5 largest funding rounds last year after Anthropic. In a world where AI makes execution easier than ever, VCs aren’t just looking for growth anymore. They’re looking for proof that you can keep it. How are you thinking about defensibility in your startup? Drop a comment—I’d love to hear your take. Image Credit: R&D World Nb: the top 4 AI companies funded last year accounted for 62% of all the funding 🫠

Why the VC model needs to change. Before, tech startups raised millions of $$$ to: ▶️ Hire a team ▶️ Build a great product ▶️ Hustle to get their first clients Now, OpenAI or Google may drop a new feature with the same functionality, bundled into a platform used by billions 🌍 That’s exactly what happened to teams building:  🎬 AI video generators  📝 Script-to-edit pipelines  ⚙️ Research-to-output workflows In this world, the strongest companies will be defined by two things: ✅ Exclusive Assets: The best AI models will become commoditized, while access to unique, defensible assets (e.g. data) will give a lasting advantage ✅ Distribution: Integration into real-world workflows, GTM effectiveness and strategic partnerships will help companies scale and create stickiness. VCs need to rethink not just what they fund, but how they help: 📊 Assets before code 📦 Distribution before intros 🤝 Partnerships before pilots Relationships, reputation, and real-world integrations are the new moats. That’s exactly why I’m excited about what we’re building at Aperiam: 1️⃣ We’re helping startups form high-leverage partnerships with legacy media and data companies, to unlock exclusive assets that would otherwise take years to access (or may not be accessible at all). 2️⃣ We help scaling. Whether it’s connecting them to major brands, or enabling growth through resellers and APIs: The goal is to make distribution and ecosystem native, not an afterthought. The next generation of venture is network-first. And that’s a feature no foundation model can replicate. #VC #AI #Advertising #Media #Tech

After my recent visit to India, I connected with several startup founders and heard stories of success, failure, and key lessons from navigating the country's dynamic startup ecosystem. As India’s generative AI ecosystem grows, with over 70 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝗮𝗻𝗱 $580 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗿𝗮𝗶𝘀𝗲𝗱, several recurring challenges emerged during my conversations: value creation, the high costs of AI infrastructure, building sustainable moats, and finding the right talent. The startups that successfully navigate these hurdles tend to focus on a few key strategies: 𝗗𝗮𝘁𝗮 𝗮𝘀 𝗮 𝗞𝗲𝘆 𝗔𝘀𝘀𝗲𝘁 It’s not just about amassing vast amounts of data; it’s about unlocking its true potential. Startups that take control of localized data and solve data-related challenges early on gain a significant edge. Understanding local nuances, structuring data effectively, and using it in ways competitors cannot create a strong competitive advantage. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗦𝘁𝗿𝗼𝗻𝗴 𝗠𝗼𝗮𝘁 Successful startups tackle complex challenges, like regulated industries and underserved markets, that aren't obvious targets. While these markets may not provide immediate returns, they offer opportunities to create lasting value, creating barriers that make it difficult for competitors to catch up. 𝗟𝗼𝗰𝗮𝗹 𝗠𝗮𝗿𝗸𝗲𝘁 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 India's market presents its own set of challenges. Startups that design products specifically for the Indian ecosystem are more likely to succeed. While there’s pressure to target global or Western markets, true success often comes from tailoring solutions to India's unique cultural, social, and economic realities. 𝗥𝗲𝘁𝗮𝗶𝗻𝗶𝗻𝗴 𝗧𝗮𝗹𝗲𝗻𝘁 India has a wealth of engineering talent, but the challenge is twofold: finding AI experts to build a strong foundation and keeping the team engaged over the long term. High churn rates are common, but fostering a culture of belonging, empowerment, and encouraging risk-taking is key to retaining top talent. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 India’s regulatory and startup ecosystem has matured significantly, offering immense growth opportunities. Successful startups capitalize on government programs, incubators, and the broader ecosystem. More importantly, they learn from founders who have failed before. Failure offers invaluable lessons, and tapping into these insights helps new startups avoid common pitfalls and chart their path to success. Source: https://lnkd.in/g-MvGMwv