Jason Cui

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Jason Cui

Jason Cui

@JasonSCui

Partner @a16z investing in infra & AI | Prev product @databricks & @uber, founder at Jemi (YC S20, acq) | Technology optimist ☀️

San Francisco, CA Katılım Ağustos 2010
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Jason Cui
Jason Cui@JasonSCui·
AI has transformed how video is created. We think the next wave is about understanding it. Over the past few years, we've seen remarkable advances in video generation, editing, avatars, and creative tooling. An increasingly important problem is teaching machines to search, analyze, reason over, and extract insight from video - across massive libraries and live streams alike. We're calling this video intelligence, and we're actively looking to back founders building here. We're most excited about companies pushing on the core capabilities: - Video-native models - multimodal embeddings, temporal reasoning, and retrieval built specifically for video rather than adapted from image or text - Real-time and large-scale pipelines - infrastructure for processing, indexing, and querying video at the speed and scale enterprises actually need - Agentic and reasoning layers - systems that don't just retrieve clips but answer questions, surface anomalies, and take action on what they see The models and infrastructure to make this real are appearing to be crossing a capability threshold right now. Multimodal foundation models are maturing, storage costs have collapsed, and enterprises are sitting on years of unstructured video with no way to use it. That infrastructure unlocks a wide range of applications including media and sports workflows, security and physical operations, enterprise knowledge management, advertising analytics, robotics, and consumer products, where video has historically been dark data. If you're building in video intelligence at the model layer, the platform layer, or in a vertical application, we'd love to talk!
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Jason Cui
Jason Cui@JasonSCui·
Our @a16z infra and events team is incredible and super special. Can’t wait for many more great events to come!
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Jason Cui
Jason Cui@JasonSCui·
Incredible to see so many talented teams build with video. Also - these amazing companies are building amazing primitives to consistently lower the barrier to build AI video products. It’s magical to see!
Jennifer Li@JenniferHli

Super fulfilled and energized hosting this 7-hour video hackathon with our friends at @fal @MuxHQ @zakariaornot at Overshoot. We’ve seen cool demos ranging from real-time moderation, to virtual tryon, to live podcasting to AI monitoring the situation. It’s a good remember how powerful video is as a computing interface and the most versatile medium. Thank you our judges @joshalphonse @Christos_antono @zakariaornot. And congrats to all the prize winners! 👏

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Jason Cui
Jason Cui@JasonSCui·
Malika is the true definition of hustle and she’s shown that in every step of her life journey. But beyond that she’s just a wonderful human. Will miss working closely with her dearly, but the next chapter will be incredible
Malika Aubakirova@MaikaThoughts

x.com/i/article/2049…

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Jason Cui
Jason Cui@JasonSCui·
@MaikaThoughts So well written and great depiction of the day to day. sad you’ll be leaving us 😭 but can’t wait for the incredible next step of your journey!!
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GTMnow
GTMnow@GTMnow_·
NEW: She's deploying $1.7 billion of @a16z's $15 billion fund into AI infrastructure, and was involved in Series A, B, C, and D for ElevenLabs. @JenniferHli (General Partner at a16z) breaks down why the firm allocated $1.7 billion of its latest $15 billion fund specifically toward AI infrastructure, and what she's betting on next. Highlights: 1:06 - Max & Paul intro: are we in a bubble? 1:46 - AI vs. dot-com era: the key differences 4:55 - B2B SaaS disruption and value destruction (Thoma Bravo / Medallia) 6:39 - Intercom / Finn: crossing the chasm from legacy to AI-native 8:08 - Introducing Jennifer Lee, a16z General Partner 9:41 - Why speed to default brand has never mattered more 11:33 - The ElevenLabs story: a16z led Series A, B, and C 12:02 - Why the seed strategy still works 13:37 - What the best founders do differently with model capabilities 15:58 - Jennifer Lee joins: why a16z raised $1.7B for infrastructure 16:22 - What existing infrastructure is being rebuilt for AI 19:19 - Specific areas a16z is focused on: models, storage, dev tools, security 20:35 - 90%+ of code now written by agents 21:52 - What Jennifer saw early in the 11 Labs / voice AI space 25:19 - Go-to-market in AI infrastructure: what's working 27:29 - Becoming the default brand: the "Kleenex effect" in AI 28:37 - What makes a founder worth backing on conviction alone 30:48 - Predictions for 2026/2027: open source catching up fast 31:39 - Most exciting new modalities: world models and vision language models 32:07 - AI and human creativity: can they coexist? 34:42 - What's blocking the creative AI future
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Tiffany Zhao
Tiffany Zhao@tiffzhao05·
I left Google DeepMind, moved from SF to NYC, all within 2 weeks to join @quadrillion_ai — to build the future of automated research intelligence with the highest slope founder and most talent dense team. I grew up in Silicon Valley — the old Facebook office was my second home. I’d hang out there after school, drawing with my crayons while looking around at the sea of computers with lines of code. Since a young age, I felt empowered to have an array of interests beyond tech: piano, ballet, figure skating, art. The valley embraced diversity of thought, and that’s what inspired me to stay for Stanford and my career thus far. But today, SF is one big hive-mind. So, I moved to NYC, away from family and friends to build a company that doesn’t need to rely on a bubble to survive. I’m meeting customers day after day in all kinds of verticals, connecting with them in different ways and seeing our product bring real value. Here, I’m able to live in diversity of thought. I’m excited to build the future of research in the city of opportunity. Let’s chat if this excites you.
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Lisha
Lisha@lishali88·
BIG PERSONAL UPDATE. I've joined a16z as a partner investing in infra and AI. I'm also stepping down as CEO of Rosebud AI. I reflect in this article on my 8 years of building in generative AI. At @a16z I‘ll be focusing on the frontier model stack: the models, and the infra and dev tooling around them. I'm excited about rapid model progress, increasingly driven by AI itself, and about what AI is unlocking for math and the sciences. And I’ll always have a soft spot for AI creative tools, having built them for 8 years.
Lisha@lishali88

x.com/i/article/2046…

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Jason Cui
Jason Cui@JasonSCui·
Incredibly excited to have @lishali88 on the @a16z infra team and collab on all things infra and AI! She's had such an incredible 8 year journey with Rosebud, and it's been inspiring to watch. Now, to an exciting new chapter together!! 🚀
Lisha@lishali88

x.com/i/article/2046…

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Malika Aubakirova
Malika Aubakirova@MaikaThoughts·
I went live on @MTSlive with @VirtualElena to talk about Continual Learning based on the article @BornsteinMatt and I recently published. Our take on why this matters, where in-context learning hits a wall, and the startup landscape forming around it 👇
MTS@MTSlive

.@MaikaThoughts says modern AI models work a lot like Memento: pre-training gives them the past, and everything after that needs scaffolding. "In Memento, the main protagonist has a form of this amnesia where he cannot form new memories. He uses sticky notes where he writes some of the notes to himself. He even tattoos some of the memories that he wants to imprint." "It kind of maps one to one to AI, how AI models work today." "We have the training phase where we basically encompass all of the world's knowledge, and that part is what we call pre-training. After the training phase, we basically have the cutoff date after which point we deploy the models into the world." "The model is basically frozen." "We use retrieval mechanisms like RAGs, we have the system prompt that essentially serves as a tattoo."

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Kimberly Tan
Kimberly Tan@kimberlywtan·
📢 We @a16z are thrilled to be leading an investment in @PetualAI, an AI platform built for audit and compliance. Compliance & internal audit are among the most resource-intensive, manual processes in public enterprises. Auditors have to wade through hundreds of unstructured files and reason through screenshots, spreadsheets, and evidence exports. This work repeats nonstop and leaves little room for higher-value activities that demand auditor judgment and stakeholder engagement. Over $8 billion is spent annually on Sarbanes-Oxley (SOX) audit in the US alone. Petual brings agentic AI to SOX testing and internal audit. The platform ingests unstructured & structured evidence to generate complete work papers in minutes, formatted to meet external auditor expectations and templates. It makes autonomous execution of evidence gathering and work paper generation possible for the first time, with built-in review to ensure human oversight remains central to the process. Part of what impressed us so much about Petual is that the company has served the largest global enterprises from the start. They have over a dozen public companies as customers, including multiple Fortune 500 and NASDAQ 100 companies. These customers report efficiency gains of up to 80% and are effusive about the product. One customer described Petual as "utterly transformative,” and another told us they'd cut budget to a quarter of what it was before. We’re also very excited to partner with founder/CEO @snirkodesh! Snir is a second-time founder who led engineering at @retool and held engineering leadership roles at @lyft prior. He’s a deeply technical builder with strong customer empathy, and he’s hired a fantastic team around him, including the former Chief Audit Executive from Lyft. He also moves with incredible urgency, as evidenced by this 1 am handshake on a Friday night as we agreed to partner 😃 This is a world-class team transforming how the largest enterprises in the world function, and we are thrilled to be backing them and to be partnering with our friends at @firstround @CowboyVC @eladgil and great angels from Retool, Lyft, @Opendoor, etc. Read more about why we invested in the comments. @BKRoberts @jamdac
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Michael Truell
Michael Truell@mntruell·
Excited to partner with the SpaceX team to scale up Composer. A meaningful step on our path to build the best place to code with AI.
SpaceX@SpaceX

SpaceXAI and @cursor_ai are now working closely together to create the world’s best coding and knowledge work AI. The combination of Cursor’s leading product and distribution to expert software engineers with SpaceX’s million H100 equivalent Colossus training supercomputer will allow us to build the world’s most useful models. Cursor has also given SpaceX the right to acquire Cursor later this year for $60 billion or pay $10 billion for our work together.

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Bryan Kim
Bryan Kim@kirbyman01·
PERSONAL UPDATE: After 5+ years, I am leaving @a16z to start a fund! This was the toughest career decision of my life. I learned from the best partners and was privileged to work with incredible founders. So why leave? It is simply time to build. More to come on this.
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SemiAnalysis
SemiAnalysis@SemiAnalysis_·
NVIDIA vLLM NVL72 ADVANTAGE: GB200 NVL72 delivers up to 3x performance compared to B200 on @Kimi_Moonshot 's Kimi K2.5. This is enabled by GB200's scale-up network which allows for frontier inference optimizations like wide expert parallelism. Great work to @rogerw0108 @NVIDIAAIDev @vllm_project @inferact @simon_mo_ ! 🚀 Not only is SGLang optimized for disagg+wideEP but vLLM is optimized too!
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Jason Cui
Jason Cui@JasonSCui·
Encouraging tokenmaxxing within large organizations seems silly at times, but it’s true that today it’s oftentimes the best proxy for tracking AI adoption and how “AI pilled” individuals are. When token subsidies ease and cost consciousness becomes more top of mind, AI spend tracking will have to get more granular and tied directly to developer productivity.
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