Garry Tan

I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.

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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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Key takeaways: - The core shift is economic, not hype. LLMs turn “AI” into a recurring operating cost, so pilots quickly become capacity planning, governance, and ownership. - Power and timelines show up because production inference needs sustained compute capacity. When usage increases across the org, you run into procurement, security review, integration work, and infrastructure readiness challenges. - The winners are not the teams with the flashiest demos. The winners are the teams that convert compute into measurable outcomes inside a workflow with traceability and controls. - In 2026, “AI strategy” that is not tied to a specific workflow, KPI, and operating model is mostly theater. If you are building the application layer on top of the datacenter buildout, execution systems, workflow automation, data-layer tooling, compliance automation, I want to see what you are shipping. Founder intro: www.axion.ventures

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🚀 Stop chasing investors blindly. Here's the 5-step system that gets startups funded: Define financial needs (12-18 months) Research aligned investors Build a killer pitch deck Systematize outreach Prep due diligence docs Most founders skip steps 1 & 5. Don't be like most founders.

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Trying something new this week - a studio industry roundup with my take on what matters. The studio model is no longer an experiment being run in a handful of places. It is infrastructure being built across geographies, sectors, and institutional types. 5️⃣ stories from the past seven days:  1️⃣ OSS Ventures hit a EUR 40M first close on a EUR 75M follow-on fund -- capital raised specifically to back the 30 companies their studio created since 2019. The "build then fund" lifecycle is maturing. B'More Venture Studio with a similar thesis: studio operations first, then dedicated capital to scale what's working.  2️⃣ Delta40 raised $20M from 54 investors across 13 countries to launch Africa's first integrated venture studio and fund. The LP base is almost entirely DFIs and foundations. Impact capital is discovering the studio model as a deployment vehicle, something I seeing across geographies (conversations with Michael Bob Starr and Damias McDonald this week alone) 3️⃣ Start Holdings launched Start.vc out of NYC and Barcelona after a $200M EV infrastructure exit. Another operator-turned-studio-founder. This is the pattern we see quite often in our work at 9point8 -- experienced operators like Jason Goldsmith who've built and exited deciding the studio model is how they want to build next.  4️⃣ UBC HATCH admitted 10 new deep-tech spinouts for Winter 2026 -- mostly climate tech. University venture builders are quietly becoming one of the fastest-growing segments. Follow Evan Allen and Jaimie Testai to keep track of its rapid evolution.  5️⃣ The INVEST Act is advancing through the Senate. If passed, it expands qualifying VC fund size from $10M to $50M and loosens general solicitation rules for accelerator and university events. For studio-funds -- and for folks in the The Venture Studio Forum community tracking policy -- this could be the most significant capital formation legislation since the JOBS Act. If this roundup format is useful, I'll keep it going. What other noteworthy news hit your desk this week, and what stood out to you?

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The most salient narrative in tech today is the end of software. Public software companies have been pummeled, down 20%+ in the last 3 months. But I think the bear case rests on weak logic. There are 3 main categories of concern driving this narrative: 1. Vibe coding means businesses build their own software 2. AI automates away jobs, and seat licenses plummet 3. AI-native companies take share from legacy incumbents The first one doesn't hold up. The second is too important to dismiss casually, but lacks evidence thus far. The third strikes me as most threatening, but it, too, rests on a flawed logical foundation.   On rolling your own software, customers who choose to build almost always regret it. An internal IT team can build a CRM, but they can't maintain and improve it like a dedicated software company. Anthropic spending 8-figures on Workday for HR software shows that incumbent providers have more to offer than software skeptics realize.   Sure, there's a risk of AI automating away seats (and therefore seat licenses). But this creates opportunity for software incumbents to deliver AI-infused software that allows customers to do just that. Software vendors can easily shift to usage-based or other value-based pricing models. Relatedly, there's a larger fear that AI will trigger massive unemployment among knowledge workers. This deserves to be taken seriously. However, unemployment is currently quite low by historical standards, and current AI is beyond what most considered AGI a few years ago. I think the worriers here are moving the goalposts. The third concern--AI-native companies decimating software incumbents--is the most formidable. 20-25 years ago, Google hoovered up the attention economy by building the dominant consumer Internet product. Could Anthropic or OpenAI do something like this in b2b? It's not my base case, but it's the scenario that goes the farthest with me.   That said, I think there are 2 misunderstandings embedded here.   The first is that only AI startups can use AI tooling to accelerate product velocity. They aren't. The engineers at Shopify and Workday have the same access to Cursor, Claude Code, or open-source AI models.   The second, bigger misunderstanding is that these markets are zero-sum. By definition, technology expands the existing pie. So I disagree with the idea that AI growth will come at the cost of existing software companies. AI will massively expand software spend.   Which brings me to what's hardest to reconcile. If you believe AI-native startups will create major value, you're implicitly underwriting a world with fragmentation and switching costs among knowledge work tools. You can't hold that public software companies are all in the blast radius of Anthropic AND back the Harveys or Open Evidences of the world.   It doesn't add up, as the bull case of one is implicitly the bull case of the other. And while the market is voting like these are opposites, I expect the weighing machine to disagree.

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Reading Metronome’s Monetization Operating Model, I kept coming back to one idea: pricing has become product. Software now delivers outcomes, not access. Yet most companies still charge as if they’re selling seats or licenses. That disconnect creates friction: for customers, unpredictability; for companies, stalled growth. The paper’s argument is simple but sharp—monetization isn’t a late-stage decision. It’s strategic infrastructure. Pricing needs the same ownership and iteration as any feature. Treat it like a surface that customers touch, not a spreadsheet buried in finance. If value is continuous and dynamic, pricing must be as well. That means product, GTM, finance, and engineering working from one system of truth. How many of us still treat pricing as an afterthought—when it should be a growth engine? https://lnkd.in/gnH7WzYf #Monetization #ProductStrategy #AI

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VC schedules, and thoughts on being a VC noob. -- VC schedules to know -- Most venture capital firms hold partner meetings on Mondays. This is typically when the investment committee votes on new investments and other key decisions. Savvy founders learn the exact timing and may need to provide additional data over the weekend to stay in motion. It is also well known that many venture capitalists observe a “European summer,” with August often being light or fully off. Similarly, the winter holidays reduce activity across much of December. That said, data from Carta shows a meaningful year-end push to close deals in December, followed by a flurry of activity in January. Founders should be cautious about kicking off fundraising during these slower periods. You do not want a raise to go stale and fall apart. VCs talk to each other, the industry is highly interconnected. -- I’m a mid career Noob! -- I am an “emerging manager,” effectively a new VC, less than three years in. In venture capital, the timeline is long. On my Linkedin you'll see I have two roles at Blitzscaling: Limited Partner means I put money into a fund, but have no decision making ability, and General Partner means I'm actively finding startups, bringing them to the partnership, and funding. As a General Partner, you are often considered an emerging manager for close to a decade, as it takes years for a fund to fully mature and establish a VC track record. Investing is a long game, but the upside can be extraordinary. Fortunately, I entered this role with a positive angel track record from my own bank account, which is often a good idea to have as a VC. When I learn a new VC concept or term, I’m an AI first, I’ll link to my post a few days ago in the comments, I totally live for constant growth and learning, if you’re not growing you’re dying. Fun fact: Many/most start their VC career in their 40s, as they need to have enough tech and biz cycles, a broad enough network, resources, and a platform to stand upon. As we end the year, I'm sharing more career thoughts, and productivity thoughts, rather than industry trends and market maps and frameworks and forecasts. I wrote all of this by hand myself, but used AI to edit some of it.

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AI is changing what “scale” actually means. In CTech by Calcalist’s 2026 VC Survey, Haim Bachar and Lee Ben-Gal share how we’re seeing the scaling metric shift - from headcount growth to output per person. This isn’t about doing more with less. It’s about building companies differently from day one - and rethinking how success is measured. See the full article below 👇

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