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ā€¢2 min read

What 'Scale' Actually Means Now

worktechlearning

before anthropic, i thought i understood scale.

i've run experiments on gpus. i've deployed apps. i've handled "big" datasets.

then i saw actual scale. like, frontier AI scale.

what i thought scale was

thousands of data points. maybe millions. running on a few gpus. taking a few hours to train.

that felt big to me as a student.

what scale actually is

think about:

  • petabytes of training data
  • training runs that take weeks
  • infrastructure across thousands of machines
  • experiments that cost substantial amounts just to run
  • coordination between dozens of people for one training run

the numbers are literally incomprehensible until you see them in action.

specific things that surprised me

1. the coordination overhead at large scale, the technical challenge is maybe 30% of the work. the rest is: communication, planning, scheduling, debugging at distance.

2. small inefficiencies matter at my scale before: a 10% slowdown is annoying. at this scale: a 10% slowdown costs thousands of dollars per hour.

optimization isn't premature. it's essential.

3. failures happen constantly when you have thousands of machines, something is always failing. the system has to handle failures gracefully by design.

4. iteration is expensive you can't just "try things" when each experiment costs significant resources. planning matters. hypothesis quality matters.

how this changed my thinking

i used to write code that worked. now i write code that works efficiently.

i used to debug by trial and error. now i think carefully before running anything.

i used to assume things wouldn't break. now i assume they will.

the meta-lesson

understanding scale isn't just about knowing the numbers. it's about internalizing that every decision has bigger consequences.

a bug at university affects my grade. a bug at scale affects production systems that millions of people use.

different weight. different responsibility.

submitted an optimization PR today. saved 0.3% on a process. at scale, that's meaningful. weird to feel proud of 0.3%.