Shahin Farshchi

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Shahin Farshchi

Shahin Farshchi

@Farshchi

@lux_capital, @zoox, @planet, @relativityspace, @vardaspace, https://t.co/tJqBBumbaX, @epsilon3inc, @nervanasys, @mosaicml, @CovariantAI, @goformic, $AEVA, Dad/Bear/Pilot

California, USA Katılım Haziran 2009
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Shahin Farshchi
Shahin Farshchi@Farshchi·
In 2006, I was in my mid-20s, had wound down my company and was doing a postdoctoral scholarship to pay my bills. I had been told that I should raise venture capital for my next startup. I googled “nanotechnology” and “venture capital,” and the first Google search result was Lux, based in NYC, founded by a couple guys my age, who had funded companies building next-gen lithography, batteries, displays, and longevity. I was FLOORED. This is at a time when SoLoMo, ad-tech, and “cloud” were all the rage. I didn’t know VCs existed in NYC, or below the age of 40. I reached out to Pete and Josh offering to help source and diligence asking for nothing but friendship/mentorship in return, as I didn’t even have an idea at the time, and they replied the same day inviting me to spend time with them. No other VC had given such a warm reply to my blind outreach. Being flat broke, I submitted papers to land speaking slots at conferences in NYC to be able to go out to see them, and the rest is history. 20 years in, I’ve had the fortune of working with talent and being part of the inception and success of amazing companies in fields investors were shunning or simply not paying attention to. I’ve learned so much from working closely with incredible people who have brought different perspectives to the firm. In 2006, deep tech felt lonely; today the talent is everywhere, but conviction still matters. It’s a privilege to be part of this next chapter with our fresh $1.5B fund to continue working with founders as they are ideating to pulling together to launching to growing to establishing generational companies accelerating our future with feats of science and engineering.
Lux Capital@Lux_Capital

We started Lux in 2000 with a simple conviction: the biggest opportunities lie at the frontier of science and technology that others find too hard, too early, or too confusing. Twenty-five years later, that conviction hasn't changed. What has changed is that the world has caught on. Now compute, automation, and biology have matured into scalable engineering disciplines, and what was once fringe "deep tech" has become essential infrastructure. New talent, new tools, and new industrial capacity now exist to turn the most cutting-edge technology across the physical, computational, and life sciences into enduring businesses. For these businesses and founders to become category-defining winners, they need meaningful capital and conviction. That is why we are especially excited to announce our largest fund to date, Lux Ventures IX, a $1.5B commitment to investing in the people turning sci-fi into sci-fact. Today entire sectors of aerospace, biotech, defense, industrials, transportation and beyond are being reinvented by a new generation of brilliantly ambitious, often irreverent, scientists and engineers. And while markets have changed and capital reshapes around fewer companies and larger checks, our focus on the craft remains constant. That focus matters because science doesn’t scale itself. The path from a result in the lab, only known to a few, to something durable the entire world will use requires a different kind of partnership. One that deploys the first $100K and the last $100M, that stays through the full arc of company-building, that mobilizes a powerful network of kinetic like-minded founders, and that treats capital as a tool rather than the product. Forty-four people at Lux now manage $7B, still operating with the same discipline and purpose we had on day one. We find and fund founders others have overlooked, back conviction over consensus, and build alongside these rebels of science and tech at every stage. We’re grateful to our valued LPs who've partnered with us through multiple cycles, and to the founders who trust us with their life's work. The mission continues, with even greater intensity and momentum. luxcapital.com/news/announcin…

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Shahin Farshchi
Shahin Farshchi@Farshchi·
@wsj: The world’s top drugmakers—operating globally and mostly headquartered in the U.S., Europe and Japan—booked $68.7 billion in deals from Chinese companies last year, more than tripling the $22.2 billion total of 2024, according to market tracker  DealForma @medra_ai will bring that spend back to the US with its incredible autonomous lab, by helping its partners develop drugs better & faster
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Unconventional AI
Unconventional AI@unconvAI·
Building a new memory means a twenty-year grind to drive the error rate toward zero. AI just made that grind optional. Because neural networks are remarkably tolerant of noise, we no longer have to chase punishing reliability targets. Training can settle into a good solution even when the hardware underneath is imperfect (which is exactly what the loss landscape below is showing). That single shift lets us stop optimizing for zero errors and start optimizing for what matters now: more bits per cell, higher density, lower energy. The universal memory never showed up, but AI may be the workload that tells each technology exactly which job it was born to do. Read the full breakdown by Giacomo Pedretti (MTS, AI Hardware Architectures) and Srenik Mehta (VP of Engineering): unconv.ai/blog/smoothing…
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Shahin Farshchi
Shahin Farshchi@Farshchi·
Broadcom’s Charlie Kawwas on co-designing custom chips: "This is why people come to the haute couture. You come to us. You tell us what kind of tuxedo or dress you want. We will customize it and build it for you and remove that general compute tax." That tax, he argued, is paid twice, in efficiency and in margin, which is why four of the five hyperscalers now spending most of the world's compute budget are moving custom at the rack level. The demand behind it is staggering: from under $1 billion in this space two years ago to a projected $100 billion-plus, and labs scaling from one or two gigawatts toward more than ten. Whoever first integrates GPU, XPU and CPU into a single platform, he suggested, may reach AGI first. Chips need to move at the speed of software, hence our investment in @cognichip whose AI models will deliver tomorrow’s chips.
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Suffiyan Malik
Suffiyan Malik@suffiyanmalikk·
My first conversation with Dan Wright (@danwrightSF), Cofounder and CEO of @armada_ai, a decentralized cloud for the edge. They are valued at $2B according to their last round and are funded by some of the friends of the show at @foundersfund , @Lux_Capital , 8090 Industries, @overmatchvc and strategic capital from firms like M12 (Microsoft's venture fund), Pinegrove and a few others. This is a special edition of our show from @RonaldReagan National Economic Forum. @danrothschild @invariant @zebulgar @Farshchi
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Shahin Farshchi
Shahin Farshchi@Farshchi·
Everyone’s building AI apps on top of chat. @monogram_ai is unlocking the power of LLMs. Ask a question, get a fully interactive visual UI back, not a bunch of text. The leap from command line → GUI transformed computing. This is that moment for AI. Proud to back @erenbali and his team once again:
Eren Bali@erenbali

It's time to get out of stealth 👋 Today, we are launching @monogram_ai and announcing our $40m seed round led by DST and Lux Capital. Monogram is the first AI app that was built around a visual interface, from the ground up. We created a technology that generates an entire user interface on the fly, in just a few seconds. Ask anything, and instead of staring at a wall of text, you get an interactive visual response.

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Lorimer Ventures
Lorimer Ventures@LorimerVentures·
🇺🇸🏭Great piece by @blsuth at @business on how Tariffs won't fix U.S. manufacturing. Access to automation (E.g. @goformic) will.👇 💰The Problem: America is the 2nd largest manufacturer in the world and 75% of the 250,000 U.S. factories have <20 employees. In '26, over 90% those don't use a single robot and basic automation can cost $100K+ with full lines costing millions. 🧠The Solution: @samanfarid and @goformic's "Netflix for robots" fixes the math: pay monthly, skip the capex, skip the maintenance headcount. 🗞️🦾Read more here: bloomberg.com/news/newslette…
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Shahin Farshchi
Shahin Farshchi@Farshchi·
@nvidia is out of proven ways to scale Rubin Ultra past a single rack: Kyber delayed to 2028, NVL72x2 dead. The compute-density arms race just hit a manufacturability wall @MajesticLabsAI made a different bet from day one: memory-first, not silicon-density-first. Stay tuned for Silicon coming soon!
SemiAnalysis@SemiAnalysis_

MASSIVE DELAY: Just 3 months after Jensen demoed Kyber NVL144 at GTC, it has faced major setbacks and has been delayed by more than 12 months, pushing it back to 2028. Below, we explain why Kyber has faced massive delays and why NVIDIA’s NVL72x2 back-to-back rack architecture was also cancelled, leaving Rubin Ultra with a limited scale-up domain. 👇️ 1/6🧵

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Shahin Farshchi
Shahin Farshchi@Farshchi·
So long as America continues to attract the very best talent from all over the planet, I’m expecting the next 250 July 4ths to continue to be celebrated in the best nation on the planet.
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Unconventional AI
Unconventional AI@unconvAI·
What if your AI chip didn't calculate the answer, but physically settled into it? That's the premise behind dynamical system hardware, and there's never been a standard way to program it. In an upcoming International Symposium on Computer Architecture (@ISCAConfOrg) 2026 paper, Unconventional AI MTS Prof. Ang Li and co-authors introduce DS-ISA: a minimalist 9-instruction architecture that bridges digital processors and continuous-time analog physics. It's a proof-of-concept, but it's the abstraction layer the field needs to build real compilers and software stacks for dynamics-based AI hardware. Full deep dive: unconv.ai/blog/speaking-…
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Modal
Modal@modal·
The most demanding problems in life sciences need more than a capable model, they need infrastructure that scales. Today we're announcing our integration with Claude Science, bringing Modal's elastic compute to researchers when they need it. We're committing up to $100K in compute to support academic life sciences research. Apply by July 15.
Claude@claudeai

Introducing Claude Science, a new app designed with every stage of research in mind. Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect. Available now in beta.

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Shahin Farshchi
Shahin Farshchi@Farshchi·
The next AI bottleneck isn't compute, it's memory TPU's inference edge over Nvidia comes down to $/TB/s, not FLOPs. Next-gen models are 5–6T parameters. Without the memory bandwidth, powerful GPUs will sit idle Chip companies innovating on memory bandwidth, like @Majestic_Labs, will win the chip wars with the servers for tomorrow's workloads h/t @SemiAnalysis_ :
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Grace Isford
Grace Isford@graceisford·
A must-read for AI builders 👇 1⃣Standardization of AI building blocks (transformers, inference APIs, harnesses) 2⃣Inference & training gains diffusing across ecosystem 3⃣Ambitious hardware innovation Plus dispelling distillation myths & how OSS advances frontier innovation 🚀
Vipul Ved Prakash@vipulved

x.com/i/article/2071…

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Shahin Farshchi
Shahin Farshchi@Farshchi·
Distributed, fast-deployable compute infrastructure beats waiting for giant grid-connected campuses:
Armada@armada_ai

Thank you to the great team at @reindsummit in Detroit for hosting Armada and the Galleon, among the companies forging a new era of American dynamism. Our Co-founder and CEO @danwrightSF talks about what's at stake in the remaining years of the AI race and what it will take to win:

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Shahin Farshchi@Farshchi·
RESPECT
Behnam Neyshabur@bneyshabur

Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei! We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others. Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems. Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains. We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them. This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab. We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us! @HarshMeh1a, @shayan_, @tararezaeikh

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Shahin Farshchi
Shahin Farshchi@Farshchi·
AI is only good as how it improves the physical world: @medra_ai and @DARPA are building agents that start with goals, to generate experiments, learn from the results, develop the next assay, and repeat:
Michelle Lee@michellearning

The future of bio is powered by faster data Introducing the Medra AI Experimentalist: an agent that turns goals into experimental designs, learns from every result, and develops the next assay Excited to collaborate with @DARPA and @NVIDIAHealth on the future of science

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Shahin Farshchi@Farshchi·
Open weights are unlocking the most consequential AI shift: frontier-class models running on-prem, at the edge, and on your person. GLM-5.2: MIT licensed, 238GB, single machine, within 5 points of Claude Opus on coding. No API. No data exposure. No latency. Massive opportunity in portable electronics, defense, and critical infrastructure. The best of this decade will be built on open source:
Z.ai@Zai_org

Introducing GLM-5.2: Frontier Intelligence, Open Weights - Significant improvements in coding and agentic tasks - Strong long-horizon capabilities with a 1M context window - Two levels of reasoning effort: GLM-5.2 (max) pushes the limits, while GLM-5.2 (high) strikes a strong balance between performance and token efficiency - MIT-licensed open weights - Same API pricing as GLM-5.1 Tech Blog: z.ai/blog/glm-5.2 Weights: huggingface.co/zai-org/GLM-5.2 API: docs.z.ai/guides/llm/glm… Coding Plan: z.ai/subscribe Chat: chat.z.ai

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Shahin Farshchi
Shahin Farshchi@Farshchi·
Fatherhood is the ultimate “you get what you put in” — and the results never lie. Grateful for the privilege, and for the great dads who showed me what sacrifice looks like. Happy Father’s Day!
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