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lisa su

T2 · manager / operator

CEO of Advanced Micro Devices since 2014; led AMD's transformation into AI and high-performance computing leader with MI300/MI355/MI450 accelerator roadmap.

3 calls·3 names·67% bull·last heard 2 months ago·TBPN
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
$AMDAdvanced Micro Devicesposition

Lisa Su details AMD's heterogeneous compute strategy and Anthropic partnership at Advancing AI

AMD's portfolio of CPU, GPU, FPGA and adaptive compute enables right-sized workloads; deep partnerships with Anthropic and OpenAI validate the MI300/MI355/MI450 roadmap and Helios rack-scale system.

TBPN2026-07episode →
2ndhigh conviction
$NVDANvidia

Lisa Su rejects winner-take-all GPU narrative, advocates heterogeneous compute for AI workloads

The AI compute market will not consolidate around a single GPU architecture; diverse workloads require CPUs, GPUs, FPGAs, and specialized accelerators working together, favoring AMD's broad portfolio over Nvidia's GPU-centric approach.

TBPN2026-07episode →
3rdhigh conviction
$ANTHROPICAnthropic

Anthropic deploys 2 GW of AMD MI450s in $5B deal and uses Claude to accelerate hardware bring-up

Anthropic's partnership with AMD for up to 2 GW of MI450 capacity signals massive compute demand; using its own models to optimize hardware bring-up (MI355) demonstrates a powerful AI flywheel that reduces time-to-market for silicon partners.

TBPN2026-07episode →

most discussed · click a bar to filter

  • $NVDA
  • $ANTHROPIC
  • $AMD

recurring themes

  • Semiconductors2
  • AI Hardware & Chip Architecture2
3 total
$NVDA
···
Nvidia
HIGHlisa su·TBPN·2 months ago·AMD CEO Lisa Su Live on TBPN
Lisa Su rejects winner-take-all GPU narrative, advocates heterogeneous compute for AI workloads
The AI compute market will not consolidate around a single GPU architecture; diverse workloads require CPUs, GPUs, FPGAs, and specialized accelerators working together, favoring AMD's broad portfolio over Nvidia's GPU-centric approach.
"every so often you hear like this is the killer chip... GPUs are going to take over the world... CPUs are dead... And the world just doesn't work like that. And so our thought pro…"
101:05
$ANTHROPIC
Anthropic
HIGHlisa su·TBPN·2 months ago·AMD CEO Lisa Su Live on TBPN
Anthropic deploys 2 GW of AMD MI450s in $5B deal and uses Claude to accelerate hardware bring-up
Anthropic's partnership with AMD for up to 2 GW of MI450 capacity signals massive compute demand; using its own models to optimize hardware bring-up (MI355) demonstrates a powerful AI flywheel that reduces time-to-market for silicon partners.
"AMD and Anthropic are partnering to deploy up to two gigawatts of AMD Instinct Mi450s... five billion dollar deal... Anthropic is on, you know, 355s and and they're doing work on…"
6:05
$AMD
···
Advanced Micro Devices
HIGHlisa su·TBPN·2 months ago·AMD CEO Lisa Su Live on TBPN· position
Lisa Su details AMD's heterogeneous compute strategy and Anthropic partnership at Advancing AI
AMD's portfolio of CPU, GPU, FPGA and adaptive compute enables right-sized workloads; deep partnerships with Anthropic and OpenAI validate the MI300/MI355/MI450 roadmap and Helios rack-scale system.
"We fundamentally believe that there is no one-size-fits-all. Like the world is a heterogeneous world... The portfolio that we have, CPUs, GPUs, we acquired Xilinx so we brought th…"
93:00
8
Semiconductorstailwind
AI-assisted chip design cuts 3-6 months from silicon bring-up, creating compounding hardware-software flywheel
Anthropic using Claude to optimize kernels for AMD MI355 demonstrates LLMs accelerating the hardware development loop; faster silicon iteration enables faster model training, which further improves chip design tools — a self-reinforcing cycle.
8
AI Hardware & Chip Architecturetailwind
AMD bets on heterogeneous compute (CPU/GPU/FPGA) over single killer chip for AI workloads
No one-size-fits-all silicon exists; AMD's portfolio approach with chiplets, networking optimization, and acquisitions (Xilinx, Pensando, ZT) enables right-sized compute per workload, validated by Anthropic/OpenAI partnerships.
8
AI Hardware & Chip Architecturetailwind
Heterogeneous compute (CPU/GPU/FPGA/NPU) wins over GPU-only for diverse AI workloads
No single accelerator dominates all AI workloads; AMD's portfolio approach — combining x86 CPUs, CDNA GPUs, Xilinx FPGAs, Pensando DPUs, and ZT Systems rack-scale integration — matches the heterogeneous reality of inference, training, and data preprocessing pipelines.
7
Semiconductorstailwind
AMD roadmap extends to MI500/MI600 with 3-5 year design cycles accelerated by AI
AI is compressing chip design cycles (Anthropic's Claude optimizing MI355 bring-up); AMD working with top customers 3-5 years out to embed workload flexibility into MI500/MI600 architectures.