Aug 17, 2026 · 48 min · 11 segments
When AI can generate and optimize code at scale, does that make software greener — or just faster to get wrong - or does it cost more to train and run those AI models then the savings we have…
Anne CurrieGuest
Andy GrabnerHost
Brian WilsonHost
And this brings me to a question, Brian, that the two of us have brought up a couple of episodes ago.
um because we had a discussion about currently we're using ai to make it easier for the human to generate code on languages that were designed to be used by a human what about if we would design a language to be used or like with the target audience of an
so could we would we now design completely different languages because we don't need to care about the human anymore that the human understands everything and needs to write it Would that lead us to better languages with better outcomes? I
think in the long run, that has to be the case, doesn't it? Or even you stop with the languages at all and move straight to getting the AI to write assembly code.
So the interesting thing there is you're both associated with... diagnostics, telemetry, tracing.
To do that, you need to basically hook up the AI to be optimizing the code based on telemetry, on information.
But then not necessarily, so the telemetry becomes incredibly important and the testing becomes incredibly important, but maybe the code isn't really all that important and certainly doesn't need to be human readable.
We also discussed, I'm not sure if you know him, and I need to quickly look him up.
And it's called the architecture of a regenerative system is defined entirely by what you can't delete.
And therefore, we spent a lot of time and we focused on the coding, not so much about writing everything down, what it's supposed to do.
But now, if we are focusing on the spec, if we do spec-driven development, then today we can decide this service should be implemented in Java, for whatever reason.
And this brings me to a question, Brian, that the two of us have brought up a couple of episodes ago.
um because we had a discussion about currently we're using ai to make it easier for the human to generate code on languages that were designed to be used by a human what about if we would design a language to be used or like with the target audience of an
so could we would we now design completely different languages because we don't need to care about the human anymore that the human understands everything and needs to write it Would that lead us to better languages with better outcomes? I
think in the long run, that has to be the case, doesn't it? Or even you stop with the languages at all and move straight to getting the AI to write assembly code.
So the interesting thing there is you're both associated with... diagnostics, telemetry, tracing.
To do that, you need to basically hook up the AI to be optimizing the code based on telemetry, on information.
But then not necessarily, so the telemetry becomes incredibly important and the testing becomes incredibly important, but maybe the code isn't really all that important and certainly doesn't need to be human readable.
We also discussed, I'm not sure if you know him, and I need to quickly look him up.
And it's called the architecture of a regenerative system is defined entirely by what you can't delete.
And therefore, we spent a lot of time and we focused on the coding, not so much about writing everything down, what it's supposed to do.
But now, if we are focusing on the spec, if we do spec-driven development, then today we can decide this service should be implemented in Java, for whatever reason.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.
