Caio Saldanha

Caio Saldanha is an AI Architect and Executive AI Advisor working at the intersection of autonomous systems and corporate governance. He architects AI solutions and behaviors within heavily regulated enterprises, and guides boards and executives through AI adoption with purpose, methodology and operational control grounded in contemporary cybersecurity principles.

Caio Saldanha

Essays

Technical debt works better as a loan book, and the missing column is the interest rate

Data leakage is how a weak model earns a strong score

A CTO's no to hype needs a test, a tripwire and a log, or it is only a mood

Build, buy or fine-tune is three decisions, and most debates merge them

Scaling laws forecast loss, and the forecast changed twice

Token prices keep falling, and AI bills keep rising: elasticity decides which wins

Inference economics shapes the product you can afford to build

Reinforcement learning works where an answer can be checked

AI sovereignty for a Latin American company is a list of dependencies you can price

The eval set is the specification

When a model solves your take-home, redesign the work sample instead of policing it

Routing between models saves money, and the exit plan is what you are really buying

Decision rights belong where the knowledge is, and AI products need them written down

Hidden technical debt in machine learning, ten years later

When prose is free, the memo worth approving names what would change the author's mind

What the Transformer changed was the order of computation

Brooks's law prices coordination, and AI code generation moved the bill to review

Blameless postmortems are a method for getting accurate facts, and the test is what changes

When your supplier is also your rival: the five forces for AI applications

Disruption theory is narrow on purpose, and most AI products fall outside it

Neither tool nor junior hire: manage AI output as a production lot with a defect rate

AI governance that ships is a set of gates in the pipeline, not a committee at the end

When a workflow beats an agent

The architect who still writes code should mostly be reading it

Gradient-boosted trees still set the baseline on tables, and the baseline is moving

A confidence score is only useful if it means what it says

A context window is not memory

Depth became trainable through a handful of unglamorous fixes

Your AI system will copy your org chart, and the eval is where the copy shows

A model that predicts well can still be wrong about what to do

The Future of Enterprise AI: Scaling Intelligent Agents

An AI strategy that starts with an ambition has skipped the diagnosis

Design Principles for AI-Native Interfaces

Most AI pilots are designed to succeed as demos and then fail as workflows

Goodhart's law is four failures, and an ML team can budget for each one

Scaffolding around a model is a bet against the next model

Seats, tokens or outcomes: AI broke the old pricing metrics

Data moats are weaker than they look, and stronger in unexpected places

Mixture of experts splits the price of a parameter in two

Hours saved are not money earned, and AI business cases confuse the two

Retrieval is an architecture decision, not a feature

Accuracy is the last thing a production model tells you