John Hwang

The reason Databricks is the largest open source company in history is both structural and execution focused. Ben gives a great overview but as an open source founder I’ll try my hand as well. Databricks is at its core a compute company. Their project Spark beat out all the other distributed data processing architectures like Hadoop and Cassandra to emerge victorious in the big data wars of the 2010s. Partially this was because the other architectures were flawed and partially because they invested more in fixing them through hiring incredible engineers. This company did come out of a Berkeley PhD lab. Still they struggled to make money. So in the mid 2010s they began making their next investment in a proprietary execution engine for Spark they called Photon. As if building the leading distributing data processing library wasn’t hard enough they also shipped a native C++ vectorized query engine and continued both the public Spark roadmap and an internal proprietary roadmap as well. It’s worth pointing out that this company is a decent example of the a16z thesis that you can kingmake with capital. Ben dropped a $10m check on a company that only asked for $250k, which is how the company had the war chest to make these investments. If that’s all they had done, my guess is they’d be a $20B or $30B company today. I’ve made the point before that open source infrastructure expands forever. That said there was a big second act. Circa 2018 they pioneered lakehouse architectures. The way to think about it that the traditional way you provision databases is you reserve some bucket on specialized compute and storage for them on your infrastructure. This made sense in a pre-cloud world where you had separate special-purpose servers, but less so in a world where you have scalable services like S3, EC2 etc. So instead of reserving expensive DB compute and storage you reserve cheap S3 storage and compute that can spin up and down based on variable usage. And now there’s a third act, namely building a cloud with AI primitives. The new gateway is a big piece of that but last month they also released a backend as a service with the primitives for building agents. So again, why is it such a big company? Again, I think this is both structural and execution oriented. The structural reasons: they started in an extremely lucrative market with a barrier to entry (it is very difficult to create popular open source projects in valuable markets!), that expands forever. Second, they are a general purpose orchestration framework. What I mean here is that every open source / devtools market is split between general purpose orchestration and specialized single purpose utilities. Think auth (Clerk) or email (Resend) for web apps (Next.js), or integrations (Composio) and sandboxes for agents (Mastra). While it is simpler from a product execution standpoint to build a single purpose utility, it is usually more valuable to build the general purpose orchestration framework and Databricks bears that out when you compare them to single purpose utilities doing things like ingestion and transformation (dbt, Airbyte). Lastly as an orchestration framework / compute company, unlike database companies they are not limited to a particular workload (Supabase etc for transactional data, Clickhouse etc for columnar data), and they are in the core infra layer as opposed to being stuck on the client (Vercel/Next.js). It’s the $10B vs $200b difference The execution reasons: they are technical geniuses who really want to win. I’ve spent the last decade building open source companies (Gatsby and now Mastra) and the typical open source founders are brilliant tinkerers and, in the best case, it takes a while to develop a ruthlessly efficient competitive commercial DNA. With Databricks they had both sets of DNA within the founding team — incredibly rare. Congrats to @alighodsi and team on everything they’ve built so far!

A company spends $10 million developing a product nobody wants. Capitalism: Congratulations, you’ve lost $10 million. Central planning: Congratulations, you’ve fulfilled your production quota. One system rewards satisfying consumers. The other rewards satisfying bureaucrats.

Once all math is formalized, it can only speed up autonomous creation of math. Actually it might be the Pandora’s box

Today we’re launching the first AI modeling agency 💅 right inside Glif. Cast hundreds of real AI models, drop your product, idea or brief and let Glif create super compelling content for you end to end. If you still think AI models aren’t ready for real work, watch this 👇

Today, Trata is excited to share the revised Hedge-Bench: our benchmark tying open-ending reasoning to prediction capabilities. We constructed 92 real-world environments derived from hedge fund analysts’ reasoning traces. Agents reason from frozen information snapshots, and their forecasts are evaluated against future real-world outcomes. No model scores above 50% (GPT-6 Astra being the top performer at 44.2%). For frontier, state-of-the-art models, these results highlight a persistent and wide gap between producing plausible-sounding reasoning and making accurate, economically valuable forecasts. In a semi-verifiable domain like finance, where outcomes are delayed and reasoning is difficult to verify, intermediate expert reasoning traces are crucial. We post-trained @Meta's Muse-Glimmer-30B on 165 environments with per-turn credit assignment using an LLM-as-a-Judge. We observe evidence of generalization: held-out forecasting accuracy at high effort improved by 10.06 percentage points, while out-of-distribution evaluation scores improved by 3.94 points on @OpenAI's GDPval, 2.29 on @Surge’s GDP.pdf, 7.07 on @Harvey’s LAB, and 4.96 on @Samaya_AI’s FrontierFinance under their respective metrics.

This is INSANE. Cisco’s president Jeetu Patel has given a 27-year-old engineer unlimited AI tokens to try to rewrite 98% of the company’s legacy code within 24 months, Fortune reports. Padmanabhan Krishnamurthy thinks he can do it with a two-person team. He has just four weeks to prove the approach.

We identified a security incident affecting a modified Claude Code VS Code extension accessed through our Cursor IDE extension marketplace, impacting at most 5,000 users. This malicious version of the extension was not developed by Anthropic. It does not impact other extensions downloaded from the cursor.com/marketplace, Grok Bot, Grok or our other SpaceXSI products. In response, we took down our extension marketplace this afternoon, worked with relevant providers to neutralize the malware, and have reinstated the marketplace after confirming there is no further risk. If affected, please review instructions in this blog post to understand how to find the malicious package and establish a path to mitigate any potential impact. We are asking all customers update to Claude Code for VS Code version 2.1.314: cursor.com/blog/claude-code-…

DoorDash says they have only received "several hundred orders in total" from personal agents like Muse.

I am excited to be an investor in trade(xyz) as they partner with ICE to bring 24/7 perpetual futures on Hyperliquid to the United States. As a market participant, I am looking forward to to using their innovative product to enhance my investment process.

SERVICENOW $NOW Q3’26 EARNINGS HIGHLIGHTS 🔹 Subscription Revenue: $4.0B (Est. $3.98B) 🟢 🔹 Total Revenue: $4.1B (Est. $4.09B) 🟢 🔹 Adj. EPS: $1.00 (Est. $1.02) 🔴 🔹 cRPO: $13.65B (Est. $13.56B) 🟢; +20.5% YoY Raises FY26 Guide: 🔹 Subscription Revenue: $15.78B-$15.79B 🔹 Subscription Gross Margin: 81% 🔹 Operating Margin: 31.5% 🔹 Free Cash Flow Margin: 36% Q4 Guide: 🔹 Subscription Revenue: $4.25B-$4.26B 🔹 cRPO Growth: 18.5% YoY Other Q3 Metrics: 🔹 RPO: $29.7B; +22% YoY 🔹 Transactions Over $5M ACV: 19; +135% YoY 🔹 Customers > $5M ACV: 680; +23% YoY Financials: 🔹 Adj. Oper Margin: 32.5% (Est. 31.2%) 🟢 🔹 Free Cash Flow: $822M Announcements: 🔹 AI ACV Target: $1.7B for 2026; +70% from the start of the year 🔹 Agentic AI: Production consumption up more than 13x YoY Comments: 🔸 “Our AI momentum continues to build, with AI net new ACV exceeding expectations and enabling us to raise our year-end target for the second time this year, from $1 billion at the start of the year to $1.7 billion now.” 🔸 “Importantly, we are capturing this significant opportunity with a capital-efficient model, reducing capex and raising our full-year free cash flow margin target by 100 basis points to 36 percent.”