Pablo Valenti

[Pablo Valenti @pablovalenbat]

AI builds silent technical debt. Prompts degrade, models shift, outputs drift. Nobody monitors because it's not visible. Document AI like code. It is code.

[Pablo Valenti @pablovalenbat]

Everyone wants the latest model. Most companies can't use the last one well. They're on 3.5 trying to figure out prompts. New model won't fix that. Master what you have first.

[Pablo Valenti @pablovalenbat]

AI is supposed to make you faster. Most teams quit week two because month one is slower. Adoption takes time. By month three it clicks. People expect speed immediately.

[Pablo Valenti @pablovalenbat]

AI training on garbage data produces confident garbage. You feed inconsistent, wrong values into the model and it sounds certain. The AI didn't fail, your data did. Fix data first.

[Pablo Valenti @pablovalenbat]

Everyone loves free AI tools until they deprecate. You're scrambling to migrate everything. Free wasn't free - it's a subscription to someone's roadmap. Build on something you control or pay for.

[Pablo Valenti @pablovalenbat]

AI adoption fails because it requires changing how people work. People hate that. Winners changed their process to fit the tool. That's leadership, not engineering.

[Pablo Valenti @pablovalenbat]

Hallucinations are obvious. Confident wrongness is the problem. AI sounds certain about something plausible but slightly off. You trust it, act on it, then realize it's wrong. Harder to catch.

[Pablo Valenti @pablovalenbat]

AI companies compete on models. They should compete on integration. The moat isn't the model. It's the ops of using it without breaking production.

[Pablo Valenti @pablovalenbat]

Companies hire AI to replace work. Then hire someone to review it. Then hire someone to manage that. They moved the work, didn't eliminate it. AI wins aren't replacement. They're in what you can do now that you couldn't before.

[Pablo Valenti @pablovalenbat]

Everyone deploys AI with confidence. The teams struggling most didn't build guardrails. The output runs in production unwatched. By the time they notice it's degraded, it's been wrong for weeks. Confidence costs more than guardrails.

[Pablo Valenti @pablovalenbat]

AI tools that stick aren't the ones with the best models.

They're the ones where someone thought about ops. What happens when it's wrong? Who catches it? How does feedback loop back?

Most teams skip this. Real AI infrastructure is about adoption and maintenance, not accuracy.

[Pablo Valenti @pablovalenbat]

$10k per month to taste ice cream and yes, this is real!

https://t.co/Phyf1pWSn8

[deel @deel]

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[Pablo Valenti @pablovalenbat]

Most AI tools fail not because they're bad tools.

They fail because nobody thought about the ops of using them.

How does the output get used? Who's responsible? What happens when it's wrong?

The teams winning with AI treat it like infrastructure, not magic.

[Pablo Valenti @pablovalenbat]

Your processes will never keep up with your growth. Stop trying to make one scale forever. Build for the size you are now. Throw it out next year.

[Pablo Valenti @pablovalenbat]

The worst ops person is always busy. The good ones are quieter. They fix root causes, not symptoms.

[Pablo Valenti @pablovalenbat]

Clean data looks true until it doesn't. Always ask where it came from. Good formatting isn't the same as good information.

[Pablo Valenti @pablovalenbat]

Feedback loops that are too long don't teach anything. Check in real time. "That didn't work, try this." A day later, not a sprint later.

[Pablo Valenti @pablovalenbat]

Speed isn't about adding process. It's about deleting it. Remove approval steps. Let people move. That's how you actually scale.

[Pablo Valenti @pablovalenbat]

The underrated ops skill: saying no. You're not blocking things. You're protecting what's working.