The "Data Debt / Decision Debt / Evaluation Debt" framing is the sharpest part. Most post-mortems blame the model or the data pipeline. The real failure is usually Decision Debt, nobody agreed on who owns the error before it happened.
The diagnostic protocol analogy lands too. Architects walking in with instinct instead of instruments is exactly why the same failure modes repeat across different orgs with different stacks.
I write about production AI systems and distributed backends, the layer where these decisions hit the infrastructure. Worth a subscribe here too.
Also in this situation/role. The biggest crazy jump is all the vibe-coded interfaces that suddenly pop everywhere with no understanding of the security disaster waiting to happen, nor of the backend work needed to make that happen securely.
There are a lot of those. Although I find enterprise customers to he very careful about vibe-coded apps. They don't put them in production without due diligence and proper security guadrails.
The "Data Debt / Decision Debt / Evaluation Debt" framing is the sharpest part. Most post-mortems blame the model or the data pipeline. The real failure is usually Decision Debt, nobody agreed on who owns the error before it happened.
The diagnostic protocol analogy lands too. Architects walking in with instinct instead of instruments is exactly why the same failure modes repeat across different orgs with different stacks.
I write about production AI systems and distributed backends, the layer where these decisions hit the infrastructure. Worth a subscribe here too.
Also in this situation/role. The biggest crazy jump is all the vibe-coded interfaces that suddenly pop everywhere with no understanding of the security disaster waiting to happen, nor of the backend work needed to make that happen securely.
There are a lot of those. Although I find enterprise customers to he very careful about vibe-coded apps. They don't put them in production without due diligence and proper security guadrails.
This is a pattern I’ve seen too. The demo vs production gap is usually less about tech breaking and more about assumptions not matching reality.
Most of the time it’s not model failure, it’s just “we thought this would hold end-to-end” and it doesn’t.
True, having the right frameworks to ask right questions in the beginning is important to shift from "we thought" to "we know".