Tiffany Luck

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

Wow, what a day in NYC! Happy 250, America! 🇺🇸 🎉

We raised $100M in Series B funding to build what comes next for video intelligence. Thank you to our investors, customers, partners, and team for helping us reach this milestone. The road to Video Superintelligence starts here. #TwelveLabs #VideoAI

@RebeccaBellan This was so fun. Thank you for having me! And I can't wait for more magical AI moments to come soon🙃

The struggle for implementing AI in enterprise is real. Thanks @lucktm for joining me on @equitypod Full episode 👇

@alex @ashleymayer Because you must stay in constant action mode to catch them! 😊

@ashleymayer How tf can parenthood make u feel younger when ur kids r small Help

Wealth can make you stingier or more generous Parenthood can make you feel older or younger Pain can make you crueler or gentler Criticism can make you more defensive or reflective Heartbreak can make you more guarded or open Failure can make you more cautious or risk seeking Luck can make you more paranoid or grateful Chaos can make you more controlling or liberated You decide.

@RMB Edition hotel lobby

Dear NYC Friends: What is a good place for a coffee meeting near Madison Sq Park? (Devocion is always too slammed.)

@maddiehfaulkner @JonLucui 💥💥💥

@JonLucui absolute killer ⚡️

Excited to share what we've been building! The next unlock isn't just better coding agents. It’s giving engineering teams the ability to steer a system where agents, automations, and feedback loops work together across the SDLC

Today, we're announcing Factory 2.0: from coding agents to software factories.

@blennon_ Exciting! Would love to try Ollie

AI can now make you a great parent. Introducing Ollie: the world’s first AI family assistant that manages your family life better than any human. Here’s how it works:

@annbordetsky @felixhhaas @PatrickHaede Love this! Serendipity ftw

Leave space for serendipity AI has brought an endless frenetic pace to everything in startups + investing I try to slam the breaks once in a while and leave room for the unexpected Like this sunny afternoon at Stanford, meeting up with @felixhhaas and @PatrickHaede, a surprise delight Just talking about the love of the game

@FDavidsonT Would love to try!!

I've spent my entire adult life thinking about travel. Built Sonder from the ground up as a college student to a $600M a year business in under 10 years. For the last six months I've been obsessing over how AI will change travel. TLDR: it will change everything. I've put together a stellar team and next week we're launching a preview of what we've built. It's been a game changer for early testers. Comment below for early access.

@maddiehfaulkner @matanSF @FactoryAI @NEA Miss you already!

When @matanSF called me and said it's time to come build at @FactoryAI, I had to answer the call. Today, I'm announcing that I'm transitioning from Partner at @NEA and Board Director at Factory to leading Strategic Initiatives at @FactoryAI full time. I will be staying at NEA as a Venture Advisor. Read more on my conviction in this team, company, and moment below. And come build with me! We need engineers, deployed engineers, gtm, strategy, bd, marketing, devrel and more: #careers" target="_blank" rel="nofollow noopener">factory.ai/company#careers

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/startu…

@RMB I wish we were closer to SF weather today. Way too cold!

What is up with the SF weather in NYC? :)

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.

@mandrusko1 💯coffee & walk as soon as real spring is here!

@lucktm Thanks Tiffany! Let's hang soon, been way too long

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/19… 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.