Arvind Jain

Why was the Fivetran-dbt merger all but inevitable? Fivetran & dbt Labs announced their merger yesterday. The all-stock deal combines two companies into an entity approaching $600 million in ARR. The beauty of the modern data stack was the explosion in choice. As the cloud exploded onto the scene, the legacy data warehouse was replaced by a collection of fast-moving platforms. In that era, specialization won. The pendulum is now swinging back towards consolidation. Why? The answer lies in compute economics & revenue scale asymmetry. The table below shows why. There are three different categories of software within this subset of the ecosystem: 1. Ingestion takes data from software & moves it into a cloud data warehouse. Snowflake acquired Datavolo, which commercializes the open source product Apache NiFi, calling it Openflow. Databricks acquired Arcion for ingestion through change data capture, calling it LakeFlow Connect. Fivetran focuses exclusively on this layer. 2. Transformation means reformatting the data within the cloud data warehouse. Snowflake launched native dbt Projects on Snowflake. Databricks offers Delta Live Tables, native SQL, & Python, plus supports hosted dbt through Databricks Workflows. dbt Core/Cloud is the leading independent transformation tool. 3. Compute revenue is generated when we ask questions of our data. Snowflake remains one of the leaders in structured data analysis with their cloud data warehouse. Databricks’ compute is their own as well. Here’s the asymmetry in one number. Compute represents 72% of the overall market ($7.6B of $10.6B). As a result of their massive operations, Snowflake & Databricks exert significant gravity within the ecosystem. They have expanded beyond the compute market to impose their presence & capture marginal revenue within customers, pressuring the competitive ecosystem. That’s not to say these components are independent. George Fraser analyzed Snowflake workloads in September 2024, finding transformation represents 40-45% of total Snowflake compute, which means even at smaller scales, startups can have significant impact on these behemoth businesses. The Fivetran-dbt merger is an inevitable evolution of a maturing market. Two unicorns must partner to compete against two decacorns. They solve two of the three customer problems. But not yet compute. One could surmise this consolidation signals the end of the modern data stack. I view it differently. The MDS has succeeded beyond our expectations. The stakes are higher now. Broad platforms, fast growth, & AI-native architectures define the next phase. Expect more consolidation. Category revenue estimates based on public disclosures & company filings. Ingestion: Informatica ($1.64B FY2024), Fivetran ($300M est.), Talend ($350M), others ($200M est.). Transformation: dbt Labs ($300M est.), others ($200M est.). Compute: Snowflake ($3.6B FY2025), Databricks ($4.0B ARR Aug 2025).

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✨ Whole Vibe: Anysphere’s AI Coding Tool Hits Different Santosh Rao just published our initiation report on Anysphere, the company behind Cursor, the AI code editor that’s revolutionizing software development. With $500MM ARR achieved in just 24 months and zero marketing spend, this represents one of the most compelling growth stories in enterprise AI. 🔑 Key Highlights: 🚀 Explosive growth from $0 to $500MM ARR in 14 months, nearly doubling every two months 👥 Over 1 million daily active users and 360,000+ paying subscribers across individual developers and Fortune 500 companies 🏢 Adopted by 50%+ of Fortune 500 companies including NVIDIA, Uber, Adobe, Stripe, OpenAI, and Spotify 💰 $1.07B total funding including $900MM Series C at $9.9B valuation (281% markup from December 2024) 🧠 Leading the “vibe coding” revolution where natural language prompts generate functional code ⚡ Exceptional team productivity: ~$5MM ARR per employee vs $125K median for private SaaS companies Why This Changes Everything: 1. Anysphere is spearheading the fundamental shift in how software gets built, with AI now generating 41% of all code globally in 2024 2. The company achieved $500MM ARR with zero traditional sales or marketing, proving exceptional product-market fit through pure viral adoption 3. Their AI-native approach and developer ecosystem create compounding network effects that become stronger as adoption scales ✍🏼 What strikes me about Anysphere isn’t just the growth metrics, it’s the paradigm shift they represent. When 92% of developers now use AI coding tools and companies like Google report 25% of new code is AI-generated, we’re witnessing the industrialization of software development. Anysphere didn’t just build a better code editor, they built the infrastructure for how all future software will be created. The fact that they reached $500MM ARR without spending a dollar on marketing tells you everything about product velocity and developer stickiness. This isn’t incremental improvement, it’s fundamental transformation of the $500B software engineering industry. MVR projects their 2025 revenue between $700MM-$1B, with a trajectory toward $2.5-3.3B by 2030. When every developer becomes 10x more productive through AI assistance, Anysphere is positioned to capture that value at unprecedented scale. Talk soon, Jc 🔗: https://lnkd.in/gn6jxFps #VentureCapital #MVPAllStarFund #TechInnovation #preipo #siliconvalley #unicorn #MVPALLSTARFUND #mvpvc #mvp #therealmvpvc #venturebytes #allstar #ipo #research #technology #tommorowsipostoday #SecondaryAsAService #saas #SecondariesAsAService #Anysphere #Cursor #AICoding #SoftwareDevelopment #DeveloperTools #ArtificialIntelligence #VibeCode #ThriveCapital #Accel #AndreessenHorowitz

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Fascinating new data from a16z on how CIOs want to buy AI apps: 1. CIOs now *prefer* usage-based models. So much for usage-based pricing being seen as a barrier to adoption 🤷‍♂️ My two cents: most AI spend w/ the CIO is on LLMs and compute. Both are usage-based categories where folks have become accustomed to granular pricing and not buying shelfware. With usage-based models, there’s also a connotation that prices will come down over time as vendors compete. Avoid picking a pricing metric that positions your app as a commodity w/o differentiation! 2. Seat-based models aren’t the key to unlocking budget. Only 21% said seat-based pricing was their preferred model. That’s slightly less than hybrid models (23%) even though hybrid models are inherently more complicated with more risk of overages/extra fees. My two cents: buying in a way that reflects the value received is >>> than having perfect budget certainty/predictability. And flexibility matters as folks are finding which AI apps are “experiments” vs ready for “production”. 3. There’s a lot of work to educate CIOs about how to pay for outcomes. 15% said outcome-based models are their preferred way to buy — although that’s still significant considering only *5% of companies* have outcome-based pricing today (based on the 2025 State of B2B Monetization report)! We’re still in the early innings. Among the concerns CIOs raised: (1) lack of clear, measurable outcomes for most products - 47%, (2) unpredictable or unscalabe costs - 36%, (3) difficulty with attribution to a specific tool - 25%. My two cents: I suspect line of business is much more open to outcome-based pricing than the CIO — after all, the CRO and the CMO are measured based on outcomes, too! But if you’re thinking about outcome-based pricing, you need outcome consistency, attribution, measurability and predictability (CAMP). — Cool to see this new data. Very fascinated to see how it changes for next year. #ai #genai #pricing

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2026 Digital Assets Look Ahead: The "Shipping Container Moment" and Industrial Retooling | Prepared by Fidelity Digital Assets Research The digital asset ecosystem is currently undergoing a profound structural transformation, moving away from its historical speculative phases toward a period of standardization and deep integration that mirrors the "shipping container revolution" in global trade. This strategic analysis by Fidelity Digital Assets Research highlights that while price action may have appeared stagnant in 2025, the underlying industry has been engaged in an intense process of "Industrial Retooling." This shift is fundamental because it moves digital assets from being mere investment vehicles to becoming the essential, albeit invisible, operating system for modern capital markets and corporate treasury management. The core significance of this period lies in the emergence of a "Shipping Container Moment," where the underlying technology begins to fade into the background as its efficiency and reliability become standardized across the financial sector. Unlike traditional analyses that focus on retail sentiment, this research identifies a critical new intersection between corporate Bitcoin treasuries and the surging infrastructure demands of Artificial Intelligence (AI). A true "game-changer" identified in the report is the evolution of Bitcoin miners into "AI Landlords." By leveraging their massive power capacities and data center footprints, these operators are transitioning from simple cryptocurrency production to providing the vital physical infrastructure required for the AI revolution. This convergence is reinforced by significant quantitative milestones that signal a move toward industrial-scale operations. A prime example is the landmark $9.7 billion agreement between the miner IREN and a major technology firm, which serves as a concrete proof of concept for the AI-power leasing model. Furthermore, the scale of capital expenditure is reaching unprecedented levels, evidenced by firms like Cipher Mining executing hardware acquisitions upwards of $5.5 billion. On the financial side, the launch of spot BTC ETF options has fundamentally altered market depth, providing the sophisticated risk management tools necessary to unlock the next wave of institutional capital and collapse previous liquidity barriers. In conclusion, the outlook for 2026 suggests a year where the radical redesign of the financial system takes precedence over simple price discovery. The mainstreaming of token-holder rights and the migration of traditional capital market functions onto blockchain "rails" are cementing digital assets as an inseparable component of global finance. As the industry moves past its "retooling" phase, the organizations that have successfully integrated these decentralized technologies into their core infrastructure will be the primary beneficiaries of this new, standardized digital economy. #Tokenization #DigitalAssets #Crypto

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🚀 🚀 🚀 Excited to announce the launch of CognitivTrust Inc. – application security reimagined for the AI coding era. Developers are shipping code faster than ever. Copilot, Claude, ChatGPT – AI is writing 40-60% of code in modern teams. We're in the vibe coding era, and it's transforming productivity. But here's the problem: our security tools haven't caught up. Current code scanners weren't built for AI-generated code: AI is generating more code in a day than teams used to write in a week. That's more surface area, more potential vulnerabilities, and more risk – all moving at unprecedented velocity. Legacy platforms are retrofitting AI as a feature, not rebuilding from the ground up. The result? Surface-level improvements that can't deliver the fundamental speed, accuracy, and transformational impact that modern security requires. We built CognitivTrust for how developers actually work today: Real-time prompt analysis that allows ‘security-ready’ AI-generated code, eliminates vulnerabilities from the IDE, contextual threat mapping while the AI is still suggesting code, and intelligent triage that works with your AI coding assistant, not against it. Security at the speed of code, not at the speed of periodical scans. What we heard at Global Fintech Festival 2025: Banking and fintech leaders are caught in an impossible trade-off: innovate at startup speed to maintain competitive edge or maintain bank-grade security while ensuring safe AI adoption by their developers. With AI coding assistants now standard, they need security that can contextualize threats in an actionable manner while keeping pace with AI-generated code. Every false positive/ security debt costs delayed product launches. Every vulnerability destroys trust. They can't afford either in the fast moving fintech or rapidly transforming banking verticals.  Ashna G.Animesh MukherjeeVishal ShuklaLars BuurNikhil SewakSahil MitalSandeep RaghuwanshiGazal GargSandeep VarmaGaurav Chaudhri Ready to secure AI-generated code without slowing down: If you're riding the AI wave and need security tools that match your velocity, let's talk. Drop a comment or DM me to see how CognitivTrust secures modern development workflows. #AppSec #AIcoding #DevSecOps #VibeCoding #CyberSecurity #DeveloperTools Russell RagarKenneth Thomas MorasRuchi KhuranaRuss D.Anshu GuptaBhaumik ShahCristian BrottoTammy SchuringMala RamakrishnanRam SenthamaraiAysha Khan

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