Amazon spends another $2.7B on Anthropic

Original Article ↗ Hacker News Discussion ↗

Amazon’s additional $2.75 billion investment in AI startup Anthropic, much of it likely in AWS cloud credits, is prompting debate over how such in‑kind deals affect valuations, competition, and Amazon’s reported revenue. Commenters weigh the strategic benefits for both sides—Anthropic gaining massive compute and Amazon securing a flagship AI partner and chip customer—against concerns that these arrangements distort market signals and entrench big-cloud dominance. The thread also branches into broader questions about the escalating cost of training frontier models, the environmental and economic limits of current scaling, and how Anthropic’s Claude models now compare with rivals like OpenAI and Google.

Structure of the Amazon–Anthropic Deal

  • Many commenters assume a significant portion of the $2.7B is AWS credits, not just cash.
  • Debate over how to value these credits:
    • One view: Amazon “spends” retail value but only incurs underlying infrastructure cost, inflating the apparent investment and Anthropic’s valuation.
    • Counterview: If credits displace paying customers, they’re effectively worth retail; the economic impact is real.
  • Some call such in‑kind, tied-to-cloud investments quasi–money laundering or misleading “gift cards”; others see them as standard, mutually beneficial strategic partnerships.

Competition, Exclusivity, and Antitrust Concerns

  • Critics argue cloud‑credit investments are anti‑competitive: they lock startups into a single cloud and distort valuations and reported revenue.
  • Defenders say:
    • These are voluntary deals between sophisticated parties.
    • Similar structures exist everywhere (e.g., sweat equity, strategic partnerships).
    • If investors or LPs can’t parse the structure, that’s their responsibility.

AWS Chips and Cloud Strategy

  • Several point out Amazon does have its own AI chips (Inferentia, Trainium via Annapurna Labs).
  • Unclear how these compare to TPUs or Nvidia GPUs, but commonly noted downside is weaker software tooling vs Nvidia.
  • Some speculate Anthropic is playing Google and Amazon off each other for better terms.

Perception of Anthropic and Model Quality

  • Multiple commenters are impressed with Anthropic’s Claude 3 (especially Opus), some preferring it over GPT‑4 for coding and writing.
  • Others highlight strong alternatives (Mistral, Cohere, local models) and note Gemini’s relative underperformance in their experience.

Local Models and Apple/PC Hardware

  • Active subthread on running open‑weight models (Mistral, Llama, Mixtral, Yi, Sterling) locally, especially on Apple Silicon.
  • Disagreement over how “blazingly fast” Apple’s unified memory GPUs really are, and whether large local models are worth it versus smaller models or cloud APIs.
  • Privacy is cited as a key reason to run local models instead of using GPT‑3.5-like cloud APIs.

Scaling, Costs, and AGI “Stall” Debate

  • Discussion on rapidly escalating training costs (from hundreds of millions potentially toward billions per model).
  • Concern: if performance gains plateau, investors may stop funding 10× annual scaling, potentially “stalling” AGI progress.
  • Others argue even flat spend with improving hardware still yields growing compute; any slowdown would be reduced exponent, not a true halt.