Andrew Powers

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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New York City-based Betaworks has closed its third fund at $66M to invest in early-stage AI startups. Betaworks, a staple of the NYC tech ecosystem since 2008, will focus its new fund on investing in pre-seed and seed-stage AI companies. The firm has been investing in AI since 2016 and has backed companies like Hugging Face. Fund III is larger than the firm's previous funds ($46M Fund II in 2020 and $48M Fund I in 2016) and saw many of its previous limited partners return to invest, a strong signal in a challenging fundraising environment for VCs. The fund will seek to make at least 25 pre-seed to seed investments, as well as 50 investments into startups as part of the Betaworks Camps program. Congratulations to the Betaworks team. TechCrunch: https://lnkd.in/d--hfMpa #VentureCapital #AI #EarlyStage #SeedFunding #NYCTech #Fundraising

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Ontologies remain one of the most effective ways to ground AI memory, keeping extracted knowledge anchored to existing structures and adaptable through feedback. As memory systems evolve, their role remains essential, and a core focus at Cognee.

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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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If you're an investor, watch this demo, seriously. Venturebase inside Anthropic's Claude is a powerful mix... Venturebase launched an MCP connector for Claude. The video demo below is compelling. Ask about a specific company, ask about an entire portfolio – get the answers you need right inside of the Claude chat experience, all based on tight permissions and visibility. Venturebase doesn’t just help investors access first-party company data, we continually try to give them better ways to think with it. Now, by integrating directly with Anthropic’s Claude, Venturebase becomes something, well…evolved: not just a system of record, but a system you can reason with. This is a frontier AI model and chat experience supercharging investor workflows. For investors, this means less time digging and more time making decisions. For founders, it raises the bar on how progress is communicated and understood. This isn’t just a new feature. It’s a shift toward AI-native investor workflows. Don’t hesitate to reach out to me to learn more. #venturecapital #startups #ai

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Quality beats brute-force slop. Edwin Chen built data-labeling company Surge AI into a $1B+ business without raising a dollar. It outpaces its closest competitor, Scale AI, in revenue, and he did it by obsessing over quality, not scale. In a rare interview, he shared his experience trying to build a sentiment analysis model while at Twitter. The first labelers they hired couldn't grok the slang, the cultural subtext of the tweets. The output sucked. That’s when it clicked: better data beats more data. As AI gets cheaper and easier to ship, the delta between mediocre and great will widen fast. And quality will be the only thing left worth betting on.

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🔗 https://lnkd.in/gmtHZtTD 🧠 Want to Build Your Own ChatGPT? Here's How. LLMs are no longer just Big Tech's playground. Thanks to open-source tools, cheaper cloud GPUs, and public datasets, you can now train your own large language model — if you're comfortable with Python, Docker, and a bit of Linux. This step-by-step guide breaks down the full journey: 🔹 Curating and cleaning massive text datasets 🔹 Choosing tokenizers and architectures 🔹 Training on multi-GPU rigs or the cloud 🔹 Fine-tuning with instruction-following data 🔹 Deploying and monitoring like a pro Forget the theory — this is a developer’s blueprint for turning raw text into a production-ready LLM. 🤖 Would you train your own model if it meant full control over cost, behavior, and brand voice? 💬 Or is it smarter to fine-tune an existing foundation? Drop your take — the DIY AI movement is just getting started.

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Conversational voice agents may represent the most significant opportunity in Vertical AI today. While many early voice AI breakouts have focused on speech-to-text transcription use cases, we believe the largest long-term opportunities will emerge from conversational, speech-to-speech uses. In today's essay, we discuss the hallmarks of early breakouts in Voice-First Vertical AI, plus our developing hypothesis on conversational voice and its potential to evolve the basic form factor of software.

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