Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google, and Amazon
Source
Lenny Rachitsky
Author
Lenny Rachitsky
Date
Terms in this piece · Glossary
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
Why it matters
If you're building AI-native products, this distills real deployment lessons from OpenAI/Google/Amazon veterans into a concrete iterative framework — including why evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → aren't a cure-all and how to build a trust-and-reliability flywheel rather than treating AI features like traditional software.
Key quotes
“You don't know how the user might behave with your product,
and you also don't know how the LLM might respond to that.”
Aishwarya Naresh Reganti
“It's not about being the first company
to have an agent among your competitors.
It's about have you built the right fly wheels in place
so that you can improve over time.”
Aishwarya Naresh Reganti
“I probably will go as far to say that
if someone's selling you one click agents
it's pure marketing, you don't want to buy into that.”
Aishwarya Naresh Reganti
“You must be comfortable with the fact
that your intuitions might not be right,
and you probably are the dumbest person in the room,
and you wanna learn from everyone.”
Aishwarya Naresh Reganti
“talking to an engineer in like any random company,
especially outside of the area,
you can see like the amount of impact
this coding agent can create
and the penetration is very low.”
Kiriti Badam
Chapters
00:00Introduction to Aishwarya and Kiriti
05:03Challenges in AI product development
07:36Key differences between AI and traditional software
13:19Building AI products: start small and scale
15:23The importance of human control in AI systems
22:38Avoiding prompt injection and jailbreaking
25:18Patterns for successful AI product development
33:20The debate on evals and production monitoring
41:27Codex team's approach to evals and customer feedback
45:41Continuous calibration, continuous development (CC/CD) framework
58:07Emerging patterns and calibration
01:01:24Overhyped and under-hyped AI concepts
01:05:17The future of AI
01:08:41Skills and best practices for building AI products
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