A coffee with… Cesar Gon, CEO, CI&T

A computer scientist at heart, Cesar Gon’s interest in coding began as an eleven-year-old in 1980s Brazil, where he then went to sell code for chess games that he’d created in the small town where he was raised.

Today Gon continues his passion for computing heading the digital transformation services firm, CI&T, in Brazil.

Gon co-founded the firm almost thirty years ago, enhancing customer experience for pizza takeaway Domino’s, improving charging experiences for car manufacturer Audi, and an AI-powered order recommender for food retailer Nestle Brazil.

Since 1995, he and his co-founder have ridden the waves of many an industry transformation, through mobile and cloud revolutions, to the current explosion in AI.

TI met Gon at a CI&T AI event hosted at the Tate Modern, where, along with trends forecaster The Future Laboratory, he spoke about the impact of AI on enterprises.

How did your relationship with computer science begin?

Very early. I began coding at the age of 11, and by 13 I was making money by selling software. I used to code games like chess and sell the software to tech magazines, earning money to buy better equipment.

This was around 1982 to 1983, at the beginning of the personal computing revolution. Finding information back then was very difficult because it was before the internet. I was in a very small city in Brazil, so the only access to information was through some computer magazines, which were quite rare then.

As someone self-educated, would you say that now is the time for businesses to make mistakes with AI to learn how best to use it?

Absolutely. This is a time of significant change, and companies must adapt. Learning about technology and consumer behaviour often comes from doing experiments, trying new platforms, products, and ideas. Given how fast things are moving, experimentation is key. Although many initiatives might fail, the main asset is the learning gained, which helps you figure out how new technologies will impact customers and strategies.

What challenges have you encountered with AI?

We’ve been playing with AI for more than a decade. In the early 2010s, around 2011-12, we were the first global partner of Google in AI. However, the foundational architectures were not mature enough for commercial applications back then.

The real impact came with the launch of GPT-2 in late 2019. Since then, I’ve spent about two hours a day studying and experimenting with AI, which required a disciplined review of my priorities.

Convincing people to move quickly to take advantage of opportunities was another challenge. AI is not just another tech hype; it requires concrete strategies and a safe environment for experimentation.

Launching our hyper-digital platform and integrating AI into everything we do has been part of our strategic response.

How are your customers experimenting with AI?

Many large companies need to see AI possibilities within a secure and reliable framework. Our role involves translating these possibilities without exposing companies to security and privacy risks. We’ve found short-term value creation in productivity enhancements, especially in IT environments, coupled with more aggressive, innovative projects for long-term learning.

Customers have seen a rethinking of legacy modernisation and interfaces, moving towards natural language interactions. Combining short-term gains with long-term learning and adapting strategies accordingly is key.

The Future Laboratory discussed the idea of ‘Protopian’ AI instead of utopian or dystopian AI – what does that mean exactly?

AI is huge, but we’re not going to see sci-fi level artificial general intelligence (AGI) in our lifetime. AI today is remarkably powerful in language manipulation and reasoning, but it’s far from AGI. The real concerns should be focused on algorithm bias, job displacement, and the misuse of AI, not on sci-fi fears.

We need to look at AI in practical terms and understand that we tend to overestimate short-term impacts while underestimating long-term effects. A ten-year period helps manage expectations and design strategies focusing on efficiency and customer experience.