Turn Assumptions Into Experiments | Generated by AI
Question: Your friend said “your biggest problem is assuming things.” Is that right?
Answer: partially right, but the wording is too broad.
From your recent discussions (MI50, ROCm, CUDA, AI training, agents, hardware choices), your strength is that you build strong mental models quickly. You often connect patterns:
- Apple ecosystem → CUDA ecosystem
- Android openness → ROCm openness
- More GPU experiments → faster learning
- Open source code → easier debugging
Those analogies are useful. Many engineers operate exactly this way: abstract from known systems, form hypotheses, test.
The risk is the jump from analogy → conclusion without measurement.
Example:
“ROCm source is open, so debugging GPU problems will be much easier.”
Maybe. But reality:
- A huge codebase ≠ easy debugging.
- Kernel/compiler/runtime issues may require deep hardware knowledge.
- CUDA has closed parts but decades of documentation, examples, StackOverflow answers, profiling tools, and production users.
Another example:
“MI50 is cheap, so it is a good AI training card.”
True in some dimensions:
- 16GB HBM2
- ECC
- lots of bandwidth
- cheap second-hand
But assumptions:
- ROCm version support
- PyTorch compatibility
- kernel support
- power consumption
- software friction
Need actual experiments.
A more scientific version of your thinking:
Hypothesis:
MI50 + ROCm can train small LLMs effectively.
Experiment:
- install ROCm
- run PyTorch benchmark
- train GPT-2 124M
- compare throughput vs RTX 4070
- measure debugging time
Conclusion:
keep or sell
That is not “stop assuming”. It is turn assumptions into experiments faster.
Your friend probably sees a pattern: you sometimes spend a lot of time building a beautiful theory before running the simplest test.
For engineering, especially AI infrastructure:
intuition → hypothesis → experiment → data → updated intuition
is the loop.
Your intuition is actually an advantage. The adjustment is: don’t let the first mental model become the final answer.