Very excited to join the team @NEA and continue to partner with amazing founders across B2B SaaS, APIs & AI! 💥

Had a really fun conversation with @bayareawriter @crunchbasenews about what makes vertical AI companies fly and what we're seeing @NEA. the tldr: there's real opportunity in the last mile! news.crunchbase.com/venture/…

Today we're announcing the Billion Dollar Build. An 8-week competition where teams will use Perplexity Computer to build a company with a path to $1B. Finalists have the opportunity to secure up to $1M in investment from the Perplexity Fund and up to $1M in Computer credits.

Excited for another great HumanX conference!! This time in SF.

Seeing a lot of mixed takes on what LLMs do to vertical software moats. Most are framed as if the biggest threat to software is "an LLM in a chat window." But the real threat is the north star: "AI Agents working with 100% reliability, enterprise context, and at institutional scale" that is slowly becoming reality. Some quick thoughts on the biggest misses I'm seeing: Business logic and institutional context are THE most important value add. Firms want expert vertical AI Agents not because they want another chatbot, but because they want something that can deliver outcomes with deep, firm-specific context. Similarly, software companies that are deeply embedded in business context have a meaningful moat. But AI's advantage is that it can be built to adapt to different types of business logic while using a shared platform. Thoughtful UI matters. UI alone is not a moat. But assuming that sophisticated AI Agents can be reduced to a chat box is a huge oversight. Users need to understand how to build Agents that best represent their workflows, how to collaborate and provide feedback, how to get them to reliably interface with other tools. Overloading all of that into a chat box just doesn't work — our lived experience at Samaya — and the need for guided, structured interaction is real. Strong engineering is still not "trivially accessible." While AI coding tools offer a real increase in productivity, building reliable, fast, scalable, and secure systems — the foundation for enterprise-grade AI Agents — is still a substantial lift. And that's not even mentioning model training work that requires deep technical understanding. AI and software teams that maintain a bar for technical excellence continue to have an important edge. With the coding tools, it's technical excellence coupled with focus and velocity. (See e.g. x.com/ibab/status/1983356398… from @ibab ) Nailing the "long heavy tail." When I was at Google, I spent a lot of time training models to be accurate on the "long heavy tail" — a large set of less common but important use cases. Fast forward to now: what's in the long heavy tail has changed, but not its existence. We consistently find small errors in our AI systems (e.g., company tickers, mixing up metrics) that have to be corrected with urgency so they don't compound as the Agent executes. I expect we will always have a changing "long heavy tail" that needs custom development. Proprietary data is not necessarily a moat. On the surface, proprietary data seems like a strong moat, especially for software incumbents. But in practice, a lot of proprietary data is not truly proprietary. It may be a data product with revenues and pressures tied to licensing it out, aggregated across different primary sources — now easier with AI — or tied to other third parties. I expect we'll see a trend towards data becoming more broadly accessible as the value accrues to what you do with the data.

Exciting news @samaya_AI — building the future of AI for finance! 🪄

This week our co-founder Soyoung Lee took the stage at the #Under30Summit 🎤 She demoed how TwelveLabs makes video as searchable as text, helping creators studios instantly: 🔎 Find exact scenes 📝 Generate metadata ⚡️ Go from raw footage → finished story faster Thanks @Forbes @zoyahasansoomro for having us! #VideoAI #TwelveLabs

Huge congrats to @maithra_raghu!! Technically brilliant, amazing leader, and a great human all around. We @NEA are beyond lucky to work with you and everyone on team @samaya_AI!

Today we're launching August, the configurable legal AI platform built specifically for midsize law firms! We've raised $7M from NEA, Pear VC, and leaders at Ramp, OpenAI, and Bain Capital Ventures. Law firms across four continents already trust us to streamline their workflows, saving hours every day. Go to august.law to learn more!

Full post here, in collaboration with @lucktm 🔗 nea.com/blog/a-call-for-100x… For founders with a differentiated take on the future, this is the time and we want to hear from you!☄️

In sports, speed = everything. ⚡ Join TwelveLabs & @Snowflake on Aug 7 to see how video AI data cloud infra is changing the game. 📊 Prep massive sports data 🎥 Auto-generate highlights ⚡ Streamline content ops 🗓 8/7 @ ⏰ 10AM PT 🔗 Save your spot: shorturl.at/kqUj3 #TwelveLabs #VideoAI

We are living in an era of moonshots & @annbordetsky and I love to brainstorm truly out of this world ideas! If you are building something 100x that goes beyond the incremental, we would love to meet and hear your vision for the future! Please reach out! nea.com/blog/a-call-for-100x…

At #AWSSummitNYC, our co-founder Soyoung Lee joined @theCUBE to break down how TwelveLabs is helping orgs: 🔍 Search decades of footage ✂️ Generate highlight reels 🛡️ Analyze surveillance 🧠 Power recommendations Watch it here: youtube.com/watch?v=PWMXvUuL… #TwelveLabs #VideoAI

With all of the amazing AI advancements, can we finally get rid of spam calls? 😑

Exciting day!! Go team! 💥

Every video frame tells a searchable story. 🎥🔍⚡️ TwelveLabs on Amazon Bedrock brings multimodal AI understanding to video content, analyzing actions, objects & background sounds. Pinpoint any detail in your video's narrative with human-like precision. #AWS #AWSSummit 👉 go.aws/4nY1B8T