Calculating the cost of a Google DeepMind paper

Scale of a $10M Experiment

• Many note that $10M is huge at individual or small-company scale but tiny for Alphabet, whose revenue and profit make such losses almost unnoticeable.
• Others argue that the real risk is not a single $10M miss but failing to learn from repeated mistakes, which could signal structural issues.

Internal vs External Compute Costs

• Multiple comments stress the article estimates replication cost at public cloud prices, not Google’s internal cost.
• Google likely used TPUs and internal quota/priority systems, making marginal cost closer to electricity and depreciation than list GPU prices.
• Some argue that if you can buy H100s outright, full ownership can be far cheaper than $3/GPU-hour cloud rates.

Electricity, Infrastructure, and Opportunity Cost

• Disagreement over whether electricity is “negligible”: for hyperscalers it’s small relative to total cost but still in the multi‑million range for heavily utilized clusters.
• Some insist opportunity cost matters: internal cycles displace revenue-generating customer workloads; others counter that utilization is not 100%, so idle capacity makes internal use very cheap.

Best-Effort / Spot Compute and Utilization

• Description of internal “best effort” or low-priority tiers: jobs run on spare capacity and get preempted by higher-priority workloads.
• Others note GPUs/TPUs are harder to preempt efficiently; frequent checkpointing and reloads can make pure “spare cycles” training unrealistic at this scale.

Reproducibility and the “GPU Poor”

• Concern that multi‑million‑dollar experiments are effectively irreproducible for most academics and small labs.
• Some compare this to high-energy physics or space experiments: expensive but still part of science.
• Worry that big labs’ compute-heavy standards crowd out “GPU poor” research and raise reviewer expectations beyond what smaller groups can afford.

Why Big Labs Publish

• Suggested motives: recruiting and retaining researchers, marketing and brand building, impressing investors, and occasionally blocking patents by placing ideas in the public domain.
• Several note that top AI papers function as high-value advertising and career currency, which can distort incentives toward hype and opaque presentation.

Comparisons and Value

• Parallels drawn to other fields: mouse studies and high-throughput drug screens routinely cost hundreds of thousands of dollars or more.
• Some argue similar or greater money is burned across industry on failed projects; research of this scale is not unusual globally.

Critiques of the Cost Estimate

• Technical commenters question assumptions on model-parallelism, hardware choice, utilization (MFU), and pricing, suggesting the blog’s dollar figure could be off by a factor (likely too high).
• Others defend the order of magnitude and emphasize that even with optimization, replication would still be extremely expensive.