Prashanth Chandrasekar

We just watched $100 billion appear out of thin air. No product built. No value created. Just an announcement. Here's how the Oracle-OpenAI deal actually works: Day 1: Oracle announces $300B mystery deal Stock jumps 42% → Larry Ellison +$100B net worth Day 2: OpenAI confirms they're the customer OpenAI revenue: $10B/year Their commitment: $60B/year Math: Does not compute Day 3: Oracle invests in OpenAI OpenAI will use that money to... pay Oracle Oracle reports "record revenues" Stock pumps again The mechanics: - Oracle gets paid whether OpenAI survives or not - Larry's gains fund the infrastructure - OpenAI gets compute now, pays later (somehow) - New investors cover old investor returns Everyone sees it: Companies investing in their customers. Customers paying with investor money. Stock prices rising on money that goes in circles. $300B for GPUs that don't exist. For data centers not yet built. For revenues not yet earned. The interesting part? Everyone knows. The VCs know. The founders know. The market knows. And everyone's still playing. Because the alternative is being the first to stop. And nobody wants to be first. So the wheel keeps turning. Not because it makes sense. But because it doesn't have to. It just has to last longer than your patience for asking questions. And historically? It always does.

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In 2010, Larry Ellison owned 23% of Oracle. Today, he owns 40%. How is that possible?     Capital Allocation. Oracle's resurgence in AI is getting alot of news but what we think is even more compelling is Oracle's understanding of capital allocation. The tech industry is often at the leading edge of innovation and business model transformation, but there's one area where the tech industry has under-optimized. #CapitalAllocation. In corporate America, capital structure is often at the core of optimizing shareholder value while in tech, it's the opposite. How often do tech founders and Boards think about the cost of equity dilution, ROIC/ROE, share buybacks and equity vs. alternative financing mix? In tech, the playbook for growth has been for decades to just raise equity throughout one's lifecycle. The result is additional dilution to founders, investors and employees. Oracle raised $32MM in their IPO and has not raised any equity since. They have raised over $150Bn+ in total debt for M&A, buybacks and general corporate uses, and repurchased ~$130Bn of their shares, offsetting equity dilution to increase equity returns.    At General Catalyst, we view the Oracle story as illustrative of a different approach tech companies can now take to build their customer base and fuel their growth. For decades, tech companies have relied solely on equity financing to fund their growth. The lack of alternative growth financing led us to create the Customer Value Fund (CVF), where we help companies of all sizes fund their sales & marketing, R&D investments and share buybacks, while minimizing shareholder dilution and risk. CVF allows companies to stop using their own cash to fund sales and marketing and instead, unlock that cash for other corporate uses, making the company more capital efficient without adding any risk to the business. To learn more, see the comments. General Catalyst #CustomerValueFund #CapitalAllocation

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The best AI apps today are being built like infrastructure companies. Great breakdown from the awesome 💡 Yazan "Yaz" El-Baba on why technical depth, rapid iteration, and margin-aware architectures are becoming table stakes for AI startups. Definitely worth a read: https://lnkd.in/gMVFFXgH 

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🚀 The AI-Native Revolution: Why Re-engineering SaaS Won't Cut It in the Enterprise 5.0 Era After decades of SaaS dominance, we're witnessing a fundamental shift that's reshaping how businesses operate and compete. This isn't just another tech upgrade—it's a paradigm change from traditional software to AI-native architectures. 💡 The Dawn of Enterprise 5.0 We're entering Enterprise 5.0, where human ingenuity combines with intelligent machines to create autonomous workflows. Unlike previous eras, employees become orchestrators of intelligent workflows rather than manual processors of information. 🏗️ Architecture is Destiny The difference between AI-native and traditional SaaS isn't in surface features—it's in architectural DNA: Traditional SaaS: Static workflows, bolt-on AI features, fragmented data AI-Native:Intelligence embedded from the ground up, continuous learning, unified data flows Adding AI features to legacy SaaS is like attaching a jet engine to a horse-drawn carriage. You might go faster, but you haven't built an airplane. ❌ Why SaaS Re-engineering Fails Three critical failure modes: 1. Fragmented Data Access - AI features only see siloed data, missing the full picture 2. Broken Learning Loops - Insights don't flow across systems, limiting compound intelligence 3. Task vs Process Automation - Bolt-on solutions automate tasks, not entire workflows 🎯 The Real Challenge Building AI-native solutions faces unique hurdles: • 85% of AI projects fail due to data quality issues • Only 12% of organizations have AI-ready data • 67% of employees show low-to-moderate AI acceptance • Requires entirely different tech stacks and organizational transformation 📈 The Market is Moving VCs have invested $2+ billion in agentic AI startups in the past two years. 25% of companies using generative AI will launch agentic AI pilots in 2025, growing to 50% by 2027. 🔮 The Path Forward Success requires: ✅ Full-stack AI rearchitecture ✅ Human-centric design that amplifies capability ✅ Different economic models (higher initial investment, lower marginal costs) ✅ Continuous learning architectures The Bottom Line:Just as budgets moved from TV to digital and software migrated to cloud, this transition will be swift and decisive. Companies retrofitting AI onto legacy architectures will be outpaced by AI-native competitors built for this new era. The future belongs to those who understand that we're not just adding intelligence to existing software—we're fundamentally reimagining how software and services work. At BGV, we're backing founders building these human-centric, AI-native solutions that combine the best of human ingenuity with intelligent machines. This is Enterprise 5.0, and architecture is destiny. #AI #Enterprise #Startups #VentureCapital #AINative #Enterprise50 #BGV #FutureOfWork --- What's your take?

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Hardware enabled software will be the most defensible companies in the AI ERA. Hardware-enabled SaaS turns sensors and devices into proprietary data engines, then converts that data into sticky, high value software with elite retention. The top companies check the boxes that Jeff Horing at Insight Partners highlighted on his recent interview on Invest Like The Best: Big ROI, Big ASP, Fast Time-to-Value. How it looks in the real world 1. Axon ($56B Market Cap) - Law Enforcement Tech What they sell: cameras and software that help police record what happened and finish paperwork faster. The Result: $2B+ Revenue and 124% Net Revenue Retention. Why it wins: devices create the evidence, Evidence.com and AI draft the reports, and multi-year bundles keep customers for years. 2. Badger Meter ($5.38B Market Cap) - Water Tech What they sell: smart water meters and software that help cities find leaks and bill accurately. The Result: $826.6M revenue in 2024. Why it wins: meters stream usage data, BEACON flags leaks and losses, and long replacement cycles plus data history raise switching costs. 3. Samsara ($22.62B Market Cap) - Safety for the Physical World -What they sell: trackers, dash cams, and software that help fleets and job sites run safely and on time. -The Result: $1.35B ARR growing mid-30s percent year over year. Gross logo retention cited at 98% plus. Net Revenue Retention 115-120%. -Why it wins: devices instrument vehicles and equipment, the Connected Operations Cloud runs daily ops, and multi-product expansion fuels retention. Why now Component costs keep falling, connectivity covers the edge, and AI is turning raw signals into decisions in seconds. This enables counter positioning. Give away or subsidize the device, price software on outcomes, and let the data flywheel deepen the moat. My simple HES playbook 1. Start with a wedge that instruments a high value, high frequency decision. 2. Remove capex friction with free or subsidized hardware that undercuts incumbents dramatically 3. Close the loop so alerts trigger real work orders in the system of record or complete the action that operators had to do historically by leveraging AI (e.g. report writing) 4. Compound moats as every deployed device grows your proprietary dataset that trains your models. 5. Sell outcomes - increased revenue, direct labor savings, insurance savings, loss reduction, etc. I'm going to share a deep dive in these businesses and my thesis in coming weeks. Let me know any other companies you admire that you think I should study!

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The LLM era is peaking. The SLM era is just getting started. Why care? 🐧 Tencent just dropped their 0.5B-parameter Hunyuan Small Language Model that can run offline on a smartphone or edge/constrained device — no cloud, no Wi-Fi, no “please hold while your AI is thinking.” 📈 It’s tiny compared to models like o4 (~200B) or o4-mini (~20B), yet it supports a 256K context window and both “fast” and “slow” thinking modes. Translation: it can produce high-quality outputs while living entirely on-device. Our AIOT portfolio companies like Atym, Mimiq and Tripolar Industries (stealth) are primed to take advantage of this wave. 🏁 Why SLMs > LLMs in certain domains: - Speed: Millisecond responses. - Offline operation: Works in connectivity deserts. - Privacy: No data leaves the device. - Focus: Perfect for specialized tasks. 🤖 Industry will be a killer app for SLMs: - Edge AI in factories: Local analysis of production data without risking IP leaks. - Aerospace & automotive: On-device AI guidance for additive manufacturing. - Frontline productivity: Real-time troubleshooting without a network tether. 🔥 Hot take: Within 3 years, most “AI in manufacturing” will not be powered by giant LLMs in the cloud — it’ll be nimble SLMs at the edge. The next AI arms race isn’t about who has the biggest model. It’s about who can make the smartest model that fits in your pocket. What’s your bet?

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Nearly all of AI’s market value sits in the infrastructure layer today, but history suggests this won’t last. Every major tech shift from cloud to mobile eventually saw value migrate to the applications that turned new technology into real-world outcomes. While foundation model companies demand massive capital and specialized talent, the application layer is wide open for builders translating AI into industry workflows. And this time, software isn’t just digitizing existing workflows, it’s automating them. AI can tap into the $10T+ spent annually on knowledge work, expanding the addressable market for enterprise software significantly. The opportunity is greater than any prior platform shift and it’s unfolding faster than ever before. To celebrate the companies defining this new wave, Redpoint is announcing the AI64 list and report. In it, we cover everything from who the biggest winners are today, the forces accelerating AI and our predictions of the next wave of winners. What you’ll take away from the report: • Why we believe AI can be at least 3x the size of the cloud market • Where AI already works and where it’s going next • How AI companies are pricing (and why outcomes based pricing hasn’t taken off yet) • How AI native GTM differs from SaaS • How AI companies are building moats  • Why vertical focus is winning in AI

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The power of distribution wins again 👑 Microsoft Copilot dominates in big enterprises. Smaller teams adopt emerging tools like Cursor, Claude AI & Zed. Cursor and Claude AI usage drops sharply as company size grows. Surprisingly, Google Gemini shows low, flat usage across all company sizes 🤔 The real innovation in AI dev tooling is happening outside big enterprises. If you're building the next-gen stack, your early adopters are in startups (and maybe medium) where there's less friction and more room to experiment.

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Garry Tan recently mentioned that forward deployed engineers at Palantir are the best candidates for starting vertical AI companies, and I completely agree. The deep customer engagement and knowledge about opaque enterprise workflows is crucial in this case. And guess who has been forward deploying engineers in US enterprises for the last 40 years? The IT services industry in India. This deep knowledge about enterprise workflows combined with an extensive bench of entrepreneurial talent in India makes India a fantastic source of vertical AI companies. We are already seeing this happen and will see more and more of this in the coming decade.

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Some things about Silicon Valley (esp Enterprise AI culture) are changing in very dramatic ways. Scrappiness, doing more with less, and everyone working mostly for equity in hopes that it would be worth a lot some day...are being replaced by some people with cold hard cash up front as compensation for time spent. This has implications beyond financing rounds etc. It has implications on incentives, company loyalty and trust, walking through brick wall attitudes, and missionary vs mercenary view of startups.

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For anyone who thinks Generative AI is not getting adopted in the enterprise fast enough, a reminder that many large companies are still early in their cloud migration journey. It's a very long game.

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2 years ago today, A16Z announced they put $100M into Pinecone, as everyone and their mother tripped over each other to invest in vector databases. Given how little standalone vector DBs are valued now, the hype cycle is wild to look back on. Quick timeline below: Late 2022: • ChatGPT launches. • Early RAG architectures emerge. • First buzz around Pinecone, Weaviate, Milvus, Chroma. Q1 2023: • Massive demand surge for vector search. • Vector DBs seen as critical infra. • Heavy VC excitement (“new database category!”). Q2 2023: • Peak hype. • Pinecone raises $100M, Weaviate raises $50M. • Every AI infra diagram has a vector DB layer. Q3 2023: • Saturation hits. • Postgres, Redis, MongoDB add native vector search. • Clouds bundle it. Q4 2023: • Hype fades. • Vector search becomes a commodity. Early 2024: • Market tightens. • Attention shifts to agents, fine-tuning, orchestration.

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Sharad Sanghi spent 25 years building Netmagic, India’s largest data center company. Now he is building Neysa, an AI cloud startup, which recently raised $50M to help businesses adopt AI, all from India. In our conversation, we discuss: 1. Why India Needs Local AI Clouds? 2. Is Scaling a Hyperscaler Easy? 3. Can India Shift from IT to AI? 4. AI in Large Enterprises vs Mid-Market 5. AI Revolution vs Cloud in 2006 6. What’s the Moat for AI Startups? 7. Is Private Data the Real AI Goldmine? 8. OpenAI’s edge on Deepseek? 9. How should enterprises navigate AI? 10. Did Only NVIDIA Predict the AI Boom? Sharad has a lot of perspective to share—as someone who was at the forefront while India adopted the internet, and now, the AI wave. If you're building in AI, part of an enterprise exploring AI, or simply thinking about where India is in the AI race—this episode is for you. Listen Now: YouTube: https://lnkd.in/gFZCzQBz Apple Podcasts: https://lnkd.in/gHsqCe9g Spotify: https://lnkd.in/gnf_aCXE

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