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Shahin Farshchi
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@Farshchi

Shahin Farshchi

@Farshchi
@lux_capital, @zoox, @planet, @relativityspace, @vardaspace, unconv.ai, @epsilon3inc, @nervanasys, @mosaicml, @CovariantAI, @goformic, $AEVA, Dad/Bear/Pilot
California, USA
luxcapital.com/team/shahin-fa…
Joined June 2009
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  • Pinned
    @Farshchi
    Shahin Farshchi
    @Farshchi
    Jan 7
    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_Capital
    Lux Capital
    @Lux_Capital
    Jan 7
    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
  • @Farshchi
    Shahin Farshchi
    @Farshchi
    Aug 28
    If “software is eating the world,” the fabs, chips, racks, interconnect, and robots are giving it teeth:
    @RaghuRaghuram
    Raghu Raghuram
    a16z
    @RaghuRaghuram
    Aug 28
    The world’s AI infrastructure is going to be reinvented. The data centers, the racks, the cooling, the power. It's the largest infrastructure opportunity in the history of computing. We raised $1.1B for it.
  • @Farshchi
    Shahin Farshchi
    @Farshchi
    Aug 27
    What if the next AI breakthrough isn’t a better model, but a better interface to the human brain? I’ll be speaking at the Subsense Forum in Palo Alto on September 2 about what it takes for BCI to scale and how AI can help. We’ll continue that conversation with Ray Kurzweil, who
  • @Farshchi
    Shahin Farshchi
    @Farshchi
    Aug 27
    Design labor, not fab capacity, is what gates new silicon. If agents write RTL, the loop from idea -> bitstream -> running hardware collapses to something like a software release cycle. Reconfigurable silicon stops being a compromise and becomes the fast path: you iterate
  • @Farshchi
    Shahin Farshchi
    @Farshchi
    Aug 26
    Disaggregating different parts of AI inference makes the memory tradeoff really interesting. SRAM-heavy architectures offer extraordinary bandwidth and locality, but capacity is expensive. @Majestic_Labs takes a different approach: putting vastly more weights within a
    @GavinSBaker
    Gavin Baker
    @GavinSBaker
    Aug 25
    Impressive that Jalapeño outperforms the comparable TPU and is in the mix with Rubin. Credit where credit is due - first good ASIC outside of TPU/Trainium. However, will likely significantly underperform a disaggregated GPU/Trainum plus SRAM accelerator setup. Especially with
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