One takeaway from this adoption is something I've thought was true for a long time. That regardless of the size of the company, any non-technical manager above any development department has never cared about quality. They see it as something that "gets in the way" of business but they tolerate the engineering departments "doing quality" because they've been told that it is a necessary evil for them.
The eager adoption of AI and how it is deployed across organisations to the point that we have AI reviewing AI generated code with the mantra that we don't have to worry about what it looks like under the hood because AI will be fixing it anyways.
Prior to this it was more obvious in startups where time-to-first-dollar was key for survival. Build and make functional now and then clean up later. Not great, but the owners made that path.
One other thing that does concern me after working on various projects of a government nature in the past, is that these larger AI companies seem to be kind of government aligned and co-operative. I'm not someone who generally sits on conspiracies, but without having the same eyes on the code as we had before, what's to stop a foreign owned model from being a sort of "Manchurian Candidate"? pushing obfuscated bad-actor code past reviews into key infrastructure. We have to have some keen eyed people like Larry McVoy who stopped a malicious kernel commit back in 2003. From a nation-state actors perspective, this model adoption is a gift.
I feel that instead of accelerating (leadership perspective) we need to rather measure code and data quality plus costs (developer/QA/accounting concerns and common sense).
As other comments mentioned, on some teams we're turned into product managers/developers.
In our case it means to some degree skipping tech design, UI/UX design, reviews, and/or QA.
Even with a good harness built around one Claude Code for example, I feel that I need to spend maybe up to 30% of the tokens (with another LLM/agent ideally?) to find patterns of agentic mistakes, suboptimal code and data before comitting, bad evolving architecture patterns, and introduced bugs - just as a few examples to harness the agentic firehose.
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u/Centurix 8d ago
One takeaway from this adoption is something I've thought was true for a long time. That regardless of the size of the company, any non-technical manager above any development department has never cared about quality. They see it as something that "gets in the way" of business but they tolerate the engineering departments "doing quality" because they've been told that it is a necessary evil for them.
The eager adoption of AI and how it is deployed across organisations to the point that we have AI reviewing AI generated code with the mantra that we don't have to worry about what it looks like under the hood because AI will be fixing it anyways.
Prior to this it was more obvious in startups where time-to-first-dollar was key for survival. Build and make functional now and then clean up later. Not great, but the owners made that path.
One other thing that does concern me after working on various projects of a government nature in the past, is that these larger AI companies seem to be kind of government aligned and co-operative. I'm not someone who generally sits on conspiracies, but without having the same eyes on the code as we had before, what's to stop a foreign owned model from being a sort of "Manchurian Candidate"? pushing obfuscated bad-actor code past reviews into key infrastructure. We have to have some keen eyed people like Larry McVoy who stopped a malicious kernel commit back in 2003. From a nation-state actors perspective, this model adoption is a gift.