Recorded at OpenAI Dev Day 2026, Ryan sits down with Andrew Ambrosino, member of the technical staff at OpenAI, for a conversation on OpenAI's strategy for merging ChatGPT and Codex into a unified experience, the advantages of localized computer access in their successful desktop application, and the technical hurdles…
Recorded at OpenAI Dev Day 2026, Ryan sits down with Andrew Ambrosino, member of the technical staff at OpenAI, for a conversation on OpenAI's strategy for merging ChatGPT and Codex into a unified experience, the advantages of localized computer access in their successful desktop application, and the technical hurdles…
In context
- Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Source: Stack Overflow Blog
- Published: 20/10/2026
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Why it matters
The conversation about artificial intelligence has become noisy, and that makes it harder to see what matters. What matters is rarely the model. It is which concrete process ends up cheaper, faster or more reliable, and who is accountable when the answer comes out wrong.
The first thing I look at is not model capability but the data feeding it. Almost every project I have seen fail did not fail on the algorithm. It failed because the data lived in three systems with three different definitions and nobody wanted to be the one to fix that.
What usually goes wrong
The mistake I have seen repeat most is measuring nothing before starting. It gets deployed, everybody feels things improved, and when somebody asks by how much, there is no answer. Without a baseline there is no way to defend next year's budget, and that is where good projects die.
What to watch
- Cost per query at real volume, not pilot volume: the variable that produces the most surprises.
- Where the data ends up and under what contract, especially when customer information is involved.
- Whether the organisation can change provider without rebuilding everything, which is the proof it did not get locked in.
How I read this entry
If I had to bring this into an organisation, I would start with a boring, measurable process — reconciliation, first-line support, document review — and get it running with a metric before touching anything flashy. The credibility for big projects is bought with a small result nobody can argue with.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
Comments
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
