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jason goodison

T2 · manager / operator

Leads General Compute, a neocloud provider building inference infrastructure on SambaNova ASICs instead of Nvidia GPUs; previously evaluated Cerebras, Groq, and other ASIC vendors.

5 calls·5 names·40% bull·last heard 2 months ago·The Information
track record

no scored calls yet — needs a stated position or a categorical verdict, with a matured window vs SPY

top calls

highest conviction · one per company
1sthigh conviction
$SAMBA-NOVASambaNova Systemsposition

General Compute bets company on SambaNova SN50: 5x faster inference, 10T params per rack, 20kW draw

SambaNova's SN50 chip enables 1-2,000 tokens/sec on frontier models with 10T parameters on a single air-cooled 20kW rack, delivering the speed and efficiency General Compute needs to own the fast inference market.

The Information2026-07episode →
2ndhigh conviction
$GROQGroq

General Compute CEO: Groq architecture fundamentally flawed for large models, 80 hops per token

Groq's attention-FFN disaggregation forces 80 round-trips per token on 80-layer models, creating insurmountable latency for large model inference despite fast single-chip performance.

The Information2026-07episode →
3rdhigh conviction
$NVDANvidia

General Compute CEO rejects Nvidia for inference: margins too high, architecture wrong for fast tokens

Nvidia's healthy margins and circular financing deals subsidize demand but its Vera Rubin/Groq architecture requires 80 back-and-forth trips per token for large models, making it fundamentally unsuited for fast inference at scale.

The Information2026-07episode →

most discussed · click a bar to filter

  • $CBRS
  • $SAMBA-NOVA
  • $GROQ
  • $NVDA
  • $AMD

recurring themes

  • AI Hardware & Chip Architecture1
  • Neoclouds & Cloud Computing1
5 total
$CBRS
···
Cerebras
MEDjason goodison·The Information·2 months ago·SpaceXAI’s Explores Texas Data Center, Oracle’s Costly Surprises, AI-Generated Movies Incoming
General Compute CEO: Cerebras fast but model size limited, cost explodes at scale
Cerebras achieves exceptional inference speed but hits a wall on large model scaling where costs become prohibitive, making it unsuitable for General Compute's frontier model targets.
"Cerebras, I think, is a really interesting uh machine. The engineers there are are exceptional. In fact, what you'll find is every ASIC company has an exceptional engineering team…"
22:35
$SAMBA-NOVA
SambaNova Systems
HIGHjason goodison·The Information·2 months ago·SpaceXAI’s Explores Texas Data Center, Oracle’s Costly Surprises, AI-Generated Movies Incoming· position
General Compute bets company on SambaNova SN50: 5x faster inference, 10T params per rack, 20kW draw
SambaNova's SN50 chip enables 1-2,000 tokens/sec on frontier models with 10T parameters on a single air-cooled 20kW rack, delivering the speed and efficiency General Compute needs to own the fast inference market.
"the ones that we're the most excited about are our Sonova chips. So, uh, we already have a cluster live with um, Sabonova's last generation hardware. We already serve models like…"
20:27
$GROQ
Groq
HIGHjason goodison·The Information·2 months ago·SpaceXAI’s Explores Texas Data Center, Oracle’s Costly Surprises, AI-Generated Movies Incoming
General Compute CEO: Groq architecture fundamentally flawed for large models, 80 hops per token
Groq's attention-FFN disaggregation forces 80 round-trips per token on 80-layer models, creating insurmountable latency for large model inference despite fast single-chip performance.
"the Grock chip is not really the optimal chip for how uh big models are getting these days. So they put every single thing on the chip. Um, and the the difficulty with what they'r…"
28:06
$NVDA
···
Nvidia
HIGHjason goodison·The Information·2 months ago·SpaceXAI’s Explores Texas Data Center, Oracle’s Costly Surprises, AI-Generated Movies Incoming
General Compute CEO rejects Nvidia for inference: margins too high, architecture wrong for fast tokens
Nvidia's healthy margins and circular financing deals subsidize demand but its Vera Rubin/Groq architecture requires 80 back-and-forth trips per token for large models, making it fundamentally unsuited for fast inference at scale.
"the Grock chip is not really the optimal chip for how uh big models are getting these days. So they put every single thing on the chip. Um, and the the difficulty with what they'r…"
28:06
$AMD
···
Advanced Micro Devices
MEDjason goodison·The Information·2 months ago·SpaceXAI’s Explores Texas Data Center, Oracle’s Costly Surprises, AI-Generated Movies Incoming
General Compute CEO: AMD inference deals with Anthropic, Meta, OpenAI make obvious sense on TCO
AMD is neck-and-neck with Nvidia on inference performance while offering lower total cost of ownership, making deals with Anthropic, Meta, and OpenAI economically rational.
"we've already started to see deals like this happen, right? So, we have Meta that's doing a deal uh with AMD already. We've got OpenAI that's doing a deal with AMD and now kind of…"
18:38
9
AI Hardware & Chip Architecturetailwind
Fast inference market emerging: ASICs like SambaNova SN50 target 1-2k tokens/sec vs GPU 50-150, Jevons paradox drives demand
A new 'broadband moment' for inference is arriving where ASICs optimize for speed (1-2k tokens/sec) rather than cost-per-token, enabling coding agents to run 10x variants simultaneously; cheaper, faster tokens expand total usage (Jevons paradox) rather than shrinking the market.
7
Neoclouds & Cloud Computingtailwind
Neoclouds diversifying from Nvidia: General Compute partners with debt financiers to fund ASIC clusters, bypassing GPU vendor financing
Neoclouds cannot rely on Nvidia/AMD circular financing (vendor-funded demand) and must partner with debt financiers to fund ASIC deployments since ASIC vendors lack balance sheets for customer financing, creating a new capital structure for AI infrastructure.