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🚀 Google just unveiled its first Gemini Founders Forum cohort, a 53 Series A, AI-first startups leveraging Gemini models for everything from healthcare and legal tech to generative content and supply chain automation. The key takeaway? Big tech wants founders to focus on deep specialization and solving real problems, agentic AI platforms are leading this transformation! this article breaks down: ✅ Which trends and niches are shaping the next wave of AI startups ✅ Why industry-focused expertise is now more valuable than general platforms ✅ How the AI-first mindset creates real business impact Read the full analysis here 👉  https://lnkd.in/eyEp4s4M #AIInnovation #agenticAI Google for Startups Google DeepMind Microsoft OpenAI Substack #startup #AIfirst Google for Developers

🚨 Altman just killed 100s of startups overnight, so let´s talk about it the right way. OpenAI quietly launched a set of new features that effectively outperformed, outpriced, and outraced dozens of startups that had built on top of its API. Overnight, founders woke up to find their entire product roadmap — or even business — obsolete. It’s brutal! But it’s also a lesson. I’ve seen this cycle before both in investing and in tech. 20 years in the markets have taught me one truth: when the foundation shifts, speed beats scale. The same thing happened in the early cloud era, in mobile apps, in Web 2.0. Today it’s AI’s turn. The problem isn’t what OpenAI did. It’s that too many startups forgot where their moat really is. If your product depends entirely on someone else’s API, pricing, or model access — you’re renting innovation, not owning it. The winners from this shakeout? Those building locally deployed, differentiated, and data-owned AI systems, not the wrappers around someone else’s API. So maybe Altman didn’t kill those startups. He just exposed who was truly building versus who was integrating. 👉 I will eagerly wait and watch what startups will do to survive: Still building on OpenAI? or the only way forward is owning their own stack?

Jungles are hot. Deserts are hot. And both are full of startups. One of the most interesting questions in tech right now is this: Where are the real opportunities—in AI startups and in deep-tech startups—and what makes each so hard? These two worlds aren’t in opposition. They’re deeply complementary. But they demand different skills, take different risks, and follow different paths to success. Let’s look at why each is hot, what makes each hard, and how founders can thrive in both. AI startups live in a crowded jungle. They can grow fast, but they’re surrounded by thousands of others reaching for the same light. They’re built on mainstream technology—open source, cloud compute, APIs, frameworks, toolkits, and now large language models. That abundance is both a gift and a curse. Anyone can build fast—but differentiation is brutal. The hard part isn’t the science so much as standing out. Your job is to find your unique patch of sunlight: a customer insight, a workflow, or a data edge others haven’t seen. Grow fast enough, strong enough, before someone shades you out. Because in the AI jungle, the question isn’t can you build it? It’s can you survive long enough to make it matter? Deep-tech startups live in the desert . They’re not fighting for sunlight—they’re fighting for water. They’re born from deep science: new circuits, materials, sensors, energy systems, quantum devices. Their challenge isn’t crowding—it’s time. Time to build. Time to prove. Time to fund. They demand specialized expertise, expensive prototyping, and long testing cycles. Years may pass before the first product or customer. But when they succeed, they reshape industries. They don’t get quick traction—they earn respect. With these startups, there's a risk spectrum. At one end: “a small matter of programming.” At the other: “fundamental science.” Between them lie algorithms, chips, sensors, materials, and processes. AI startups play at the fast-iteration end—rapid growth, intense competition. Deep-tech startups live at the slow-burn end—high barriers, huge payoff. That’s why I built my Startup Bootcamp — a free online course for ambitious technologists and entrepreneurs who want to build something truly impactful. Across 15 live sessions, Nov 3 – Dec 12, 2025, we explored both sides of the frontier: how to turn deep technology into business impact, and how to carve out an enduring edge in the AI explosion. It’s designed for engineers, scientists, and product leaders who’ve been in the trenches— and want to understand how great startups grow from raw innovation to real scale. Now the same lectures are available in a self-paced course. Take a look at my latest video on this startup contrast: https://lnkd.in/gwkzWZqG Link to the startup bootcamp course: https://lnkd.in/g52G-CJG #AI #DeepTech #Startups #Entrepreneurship #Innovation #StartupBootcamp #Leadership #Founders #TechStrategy

AI startups are outgrowing their teams faster than their SaaS counterparts. When we started Lily AI years ago, moving with urgency set us apart. Today, it’s the baseline. Everyone is fast, everyone is hungry. What used to take years now happens in months, sometimes weeks. But here’s the paradox of that pace: the faster the market moves, the faster your experience becomes obsolete. That’s where most companies struggle. AI-native startups that hire purely for experience often realize that experience gets outdated within quarters. The real edge now isn’t tenure, it’s taste, agency, and comfort with chaos. That’s why I’m deeply grateful for the people around me at Lily, who don't just have the skills, they have the mindset. They are great at what they do, but what truly sets them apart is their velocity to learn faster than others in the industry, that comes with deep ownership of the mission, their craft & focus on the outcome. In AI, the shelf life of what you know keeps getting shorter. And having a team that can outpace the growth of company is your unfair advantage. PS: here's my team of heavy hitters at NYC. Maria Pastore Cyndi Collins Julian Dimery James Beauchamp Alex Vitale Sumi Scott

Ex-Google DeepMind founders just raised $2B at $8B valuation in 12 months. Reflection AI co-founders Misha Laskin and Ioannis Antonoglou secured one of the largest open-source AI funding rounds ever. The Brooklyn-based startup is building superintelligent foundation models to rival Meta's Llama and China's DeepSeek. • $2B Series B at $8B valuation • Led by NVIDIA, B Capital, Citi, DST Global • Eric Schmidt's Hillspire family office participated • Total funding: $2.13B in just over 1 year • Mission: democratize AI through open-source models How it happened / Founder steps: • Left Google DeepMind with proven AI research track record • Positioned as "open-source alternative" to closed AI labs • Secured initial backing from top-tier VCs (CRV, Lightspeed, Sequoia) • Built credible technical team from leading AI research institutions • Attracted strategic investors (NVIDIA) who need open-source AI ecosystem • Leveraged timing: market demand for AI democratization vs. closed models Takeaway: Technical credibility + open-source positioning + strategic timing = massive funding even for early-stage AI labs. Source: TechStartups.com Brewed by Startup Beaker. 🚀

With the rise of AI-native startups, we’re seeing a wave of products entering the market that buyers simply don’t know how to place. There’s no clear category. No familiar comparison. No “Oh, it’s like X for Y.” This creates a real challenge: ➡️The target audience might not even realize they have a problem. ➡️They can’t visualize where your product fits. ➡️They certainly won’t buy just because the technology is impressive. Most buyers need a frame of reference. They need to connect your product to something they already understand. Without it, they’re left confused; or worse, indifferent. Too often, early-stage startups make the mistake of going deep on features and tech. They try to sell the "what" without ever clarifying the "why". This leads to confused buyers, missed expectations and high churn. When your product is truly new, it’s your job to educate the market and build that reference frame from scratch. How to do it well: ➡️Anchor to existing mental models ➡️Define a clear, compelling problem (Problem > Solution) ➡️Create your own category language (but not too early) ➡️Build early social proof ➡️Continuously educate the market ➡️Use narrative-led positioning ➡️Start narrow and then scale ➡️Create demand entry points ➡️Speak the customer’s language, not your own Whether you’re building a new category or disrupting an old one, it doesn't matter. Don’t lead with features. Lead with value, clarity, and context. Otherwise, you risk losing the game before it even starts.

With the rise of AI-native startups, we’re seeing a wave of products entering the market that buyers simply don’t know how to place. There’s no clear category. No familiar comparison. No “Oh, it’s like X for Y.” This creates a real challenge: ➡️The target audience might not even realize they have a problem. ➡️They can’t visualize where your product fits. ➡️They certainly won’t buy just because the technology is impressive. Most buyers need a frame of reference. They need to connect your product to something they already understand. Without it, they’re left confused; or worse, indifferent. Too often, early-stage startups make the mistake of going deep on features and tech. They try to sell the "what" without ever clarifying the "why". This leads to confused buyers, missed expectations and high churn. When your product is truly new, it’s your job to educate the market and build that reference frame from scratch. How to do it well: ➡️Anchor to existing mental models ➡️Define a clear, compelling problem (Problem > Solution) ➡️Create your own category language (but not too early) ➡️Build early social proof ➡️Continuously educate the market ➡️Use narrative-led positioning ➡️Start narrow and then scale ➡️Create demand entry points ➡️Speak the customer’s language, not your own Whether you’re building a new category or disrupting an old one, it doesn't matter. Don’t lead with features. Lead with value, clarity, and context. Otherwise, you risk losing the game before it even starts.

🚀 Game-Changer Alert: Google Startup Hub Launches at T-Hub, Hyderabad! This is a massive leap for the Indian startup ecosystem! Google and T-Hub are officially launching the Google Startup Hub in Hyderabad, with a sharp focus on nurturing the next generation of AI-First Startups. Key Highlights & Impact: AI Acceleration: This Hub is designed to be a premier breeding ground for AI-first startups, specifically those solving real-world challenges in climate, health, and education. Global Bridges: The partnership will create direct pathways to international markets and capital by bringing in top VCs from Silicon Valley, Japan, and Southeast Asia, as well as global corporate innovation leaders. Expert Deployment: Google will deploy its senior experts across AI/ML, Product, UX, and Go-to-Market (GTM) domains to conduct in-depth sessions, directly mentoring startups and developers. Inclusive Innovation: The program plans to run tailored tracks for women entrepreneurs, Tier-2 innovators, and university talent, aiming to instill an early innovation mindset in over 10,000 students annually. Tangible Outcomes: The government expects over 200 new startups to be launched by next year, with 30% of them focusing on AI/Deep-Tech. The initiative is projected to attract over ₹50 crore in funding through the joint investor networks. Economic Vision: This strategic collaboration is a foundational step towards Telangana's ambitious goal of becoming a $1 trillion economy by 2034. What Startups Get: Startups will be equipped through special AI-focused bootcamps and innovation days, granting them access to: Google's Cutting-Edge Tools and AI/ML Capabilities Essential Cloud Credits Direct engagement with successful AI startup founders This is more than just a launch; it's a strategic alliance setting the pace for India’s digital future! #AICapital #StartupIndia #THub #Google #AIStartups #Hyderabad

Google picks 20 AI-first startups for its “Accelerator: AI First” program in India 𝗪𝗵𝗮𝘁 𝗶𝘀 “𝗚𝗼𝗼𝗴𝗹𝗲 𝗳𝗼𝗿 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿: 𝗔𝗜 𝗙𝗶𝗿𝘀𝘁”? It’s a 3-month accelerator program for Seed-to-Series A AI startups in India. Selected founders get access to Google Cloud infrastructure, advanced AI tools (including Gemini), technical and business mentorship, plus go-to-market support. The program emphasises responsible AI, scalability, and real-world impact across sectors. 𝗪𝗵𝗮𝘁 𝗸𝗶𝗻𝗱𝘀 𝗼𝗳 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝗮𝗿𝗲 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗖𝗼𝗵𝗼𝗿𝘁? The 20 selected startups are working on cutting-edge use cases like:   • Agentic AI platforms for enterprises   • Healthcare tools (e.g. remote autism screening, smart stethoscopes)   • DevTools for faster app development and generative workflows   • Finance & fintech tools automating back-office work, reconciliations, payments, etc.   • AI in education, voice tech, and productivity tools 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 / 𝗜𝗺𝗽𝗮𝗰𝘁   • Accelerates innovation: Founders gain resources to go from prototype to market scale faster.   • Focus on India's AI leadership: Helps build startups that contribute to foundational and multimodal AI capabilities.   • Addresses large societal needs: Healthcare, education, finance, climate, etc., get AI-driven enhancements. At Startup Veda, we decode the startup signals behind these announcements, so you can see what matters for building, investing, and scaling in AI. Follow us for stories that map not just what’s funded, but what’s forming the future. #AI #GenAI #Founders #news #startup #startups #indianstartups #google #work

OpenAI CEO Sam Altman said he’s “jealous” of today’s 20-year-old dropouts, calling this the best era to build startups. With AI tools lowering barriers and VCs backing young founders, he said innovation opportunities are wider than ever. Read More: https://lnkd.in/dtj3gfrd #OpenAI #SamAltman #Startups #Digital #Technology