Issue No. 40Week ending Sunday, October 4, 2026485 episodes · 2075 articles
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AI infrastructure and compute

Andrew Feldman on AI infrastructure and compute

7 quotes from 2 episodes on All-In Podcast, each with a timestamped link to the source.

7 quotes2 episodes

The short version

Andrew Feldman states that current AI hardware cannot keep up with demand, forcing the industry to invent new computing architectures. The global buildout now requires individual data centers the size of football fields that consume more power than midsize cities.

Most interesting insights

Surpassing Nvidia on traditional processor designs is nearly impossible, prompting companies to invent entirely different hardware.

“…the odds that you're better than Nvidia in our view are approximately zero.”

Andrew Feldman, All-In Podcast · June 2026 · Watch at 23:09 ↗

From Andrew Feldman: AI Needs a Dinner Plate Chip, Not GPUs

Establishing communication networks between computing clusters in space remains an unsolved engineering challenge.

“We we're not super good yet at building the clusters in space necessary for the communication between Exactly.”

Andrew Feldman, All-In Podcast · June 2026 · Watch at 18:21 ↗

From Marshall: Space Compute Cheaper Than Earth Within a Decade

Top talking points

  1. Demand for compute hardware outstrips supply

    The industry cannot build facilities fast enough to meet current compute requirements. Individual data centers now span the area of football fields and draw more power than midsize cities.

    “The demand is way outstripping our ability to build data centers and to fill them with hardware.”

    Andrew Feldman, All-In Podcast · July 2026 · Watch at 3:41 ↗

    From AI's $25 Billion Shortage: Compute Is The New Oil

    “We're talking about individual buildings the size of football fields that have more power coming into them than midsize cities…”

    Andrew Feldman, All-In Podcast · July 2026 · Watch at 2:26 ↗

    From AI's $25 Billion Shortage: Compute Is The New Oil

  2. Outperforming rivals requires completely new architectures

    Achieving 20 times better performance demands fundamentally different systems because established competitors have already consumed all the easy architectural improvements.

    “Our view as computer architects is if you want to be 20 times better than somebody, your architecture can't look like them. They have enjoyed and eaten all the lowhanging fruit.”

    Andrew Feldman, All-In Podcast · June 2026 · Watch at 22:52 ↗

    From Cerebras's Feldman: Outpace GPUs 18x by Rewriting AI Silicon

  3. Moving data creates a major computing bottleneck

    The central problem in AI processing is transferring data between memory and compute components. Faster data movement directly results in quicker answers and a better user experience.

    “…the hard part is moving data from memory to compute. This is the fundamental problem in AI.”

    Andrew Feldman, All-In Podcast · June 2026 · Watch at 23:19 ↗

    From Andrew Feldman: AI Needs a Dinner Plate Chip, Not GPUs

    “That means your answers are delivered more quickly. It means your engagement with the AI is more enjoyable.”

    Andrew Feldman, All-In Podcast · June 2026 · Watch at 24:01 ↗

    From Cerebras's Feldman: Outpace GPUs 18x by Rewriting AI Silicon

Key takeaways from these write-ups

AI's $25 Billion Shortage: Compute Is The New Oil

  • The global AI infrastructure buildout is historically unprecedented, drawing comparisons to ancient mega-projects like the Great Wall in terms of capital and talent, as All-In Podcast host Jason Calacanis observed.
  • This construction includes data centers the size of football fields, consuming power equivalent to mid-size cities, now emerging everywhere from the US to nations like Kazakhstan and Georgia.

Marshall: Space Compute Cheaper Than Earth Within a Decade

  • Within a decade, most global compute will move off-planet, becoming a multi-trillion dollar industry larger than any other space business today, predicts Planet Labs CEO Will Marshall.
  • This shift is driven by economics: Starship will drop launch costs to an unprecedented $200-$300 per kilogram, making space data centers financially viable.

Andrew Feldman: AI Needs a Dinner Plate Chip, Not GPUs

  • GPUs, despite their market dominance, are fundamentally insufficient for the real-time demands of advanced AI due to a bottleneck in moving data between memory and compute.
  • Cerebras Systems, under Andrew Feldman, took a contrarian bet: instead of optimizing existing GPU architectures, they designed a radically new system around a single, massive "dinner plate-sized" silicon chip.

How we attribute quotes. Every quote was matched against the episode transcript, so the words and the timestamp are real (we trim filler words like "um", nothing else). The name comes from our written summary of the episode. YouTube gives us no voice-by-voice transcript, so open the timestamp to hear who is talking. See a wrong name? Tell us and we fix or remove it.

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