He Watched AI Take His Job. Now He’s Teaching MBAs To Stop Fearing It

Pepe Alonso was working at a startup in the Bay Area – one of several he’d joined since leaving Uruguay, drawn by the pace and the proximity to people actually building things – when he recognized something that hadn’t yet registered with most of his colleagues. 

Alonso saw that the code he was writing, and the engineering skills he’d spent a decade honing, were quietly being made redundant by the same technology his industry was celebrating.

“I knew that this will come for my job in anything that I don’t teach,” he says.

So he enrolled in a master’s program in artificial intelligence. While applying for his MBA.

THE BADGE PROBLEM

Alonso is now the vice president of education for the AI Club at the Haas School of Business at UC Berkeley, and, as of this summer, an intern at Google working on AI agent workshops and internal automation tools. Unusual for an MBA student, he is also slated to teach a module in Haas’s Fundamentals of AI class in the fall, lead a formal elective of his own, and co-instruct a section in an EECS course. 

He is, in other words, teaching engineers and business students simultaneously, from both sides of a divide he has spent years trying to close.

The decision to pursue an MBA alongside a master’s in AI reflected a calculation that had less to do with business fundamentals than with what Alonso calls the unspoken rule of the Bay Area startup ecosystem.

“When they say they’re investing in people, they invest in stamps of the people,” he says. “Are you ex-Uber? Are you ex-Amazon? Are you ex-McKinsey? Berkeley, Stanford, Harvard?”

As a foreigner building a career in a city that runs on credentials, Alonso concluded that even strong AI expertise on its own wasn’t enough. But by the time he was deep into MBA application essays, something else had shifted. His software engineering work had changed so drastically that he began to see the MBA itself as insufficient, too – at least as it was being taught.

The argument was concrete enough that he built it into his application to Haas – a presentation summarizing the uptick in MBA internship postings that specified machine learning experience, not just AI fluency. He was pitching the school on a curriculum gap before he’d been admitted to fill it.

THE FORCE MULTIPLIER

The insight that emerged was sharper: AI was not coming for everyone equally. Business school students, he decided, were better positioned to benefit from it than engineers.

“MBA students are way more aligned to this new AI world,” Alonso says – a claim he is prepared to argue on technical grounds.

His reasoning starts with what large language models actually are: tools that amplify existing capabilities and translate effort into outcomes companies can measure. A software engineer optimizing a server for speed isn’t necessarily moving a business metric. An MBA graduate who understands how AI connects systems – integrating an unstructured interview with a spreadsheet to generate qualitative data at scale, for instance – is operating closer to what companies actually need right now.

There’s also the question of how AI works at a fundamental level. The stochastic, probabilistic nature of LLMs, Alonso argues, is better understood through the lens of statistics, economics, and data analysis – the quantitative backbone of MBA training – than through the deterministic logic of traditional software engineering. “It’s more about regressions and statistics,” he says.

ZERO TO AI AGENTS

None of that explains what happened when he tried to share it with his classmates.

He had been informally helping a handful of students when he decided to run a workshop series last spring, which he called From Zero to AI Agents. He structured it across four sessions – software engineering fundamentals, AI fundamentals, AI for work, and the frontier of multi-agent systems – and expected four or five people to show up. Each session ran roughly 100 slides. By the fourth week, 80 students were attending on their own time. Several told him afterward that what they’d learned had helped them land jobs or survive technical interviews.

The demand wasn’t accidental. Alonso had researched the market before pitching the workshops to Haas. His read of MBA internship postings had turned up a specific signal: companies weren’t just asking for familiarity with AI tools. They were asking for machine learning and AI experience – which, to anyone with a technical background, means something far more demanding.

“If they say machine learning, not just AI, they don’t mean you understand AI use cases or talk to ChatGPT,” he says. “They mean you know how to deploy a system.”

He frames what’s happening as a kind of category error at the industry level – companies panicking and reaching into business schools for help, not quite sure what to ask for, but certain they need people who understand both the technology and the business problem it’s meant to solve. “They want entrepreneurs – AI native minds to come and help them,” he says. “Somebody, please help us.”

FUNDAMENTALS, NOT TOOLS

The class Alonso will teach this fall, Fundamentals of AI, takes its name seriously. He is explicit that he has no interest in teaching students to use tools that may not exist in two years. His concern is the underlying architecture: how LLMs work, how agent systems are structured, why the tech has evolved as it has.

The worry underneath that approach is real. He believes too many programs are responding to AI by assigning case studies about AI companies – producing, in his telling, executives who understand a product they’ve never actually used.

“You cannot make a decision for a company where you do not fundamentally understand the product,” he says. “It’s like being the CEO of McDonald’s and never tasting a burger. Just knowing the concept of a burger, but never tasting what it means.”

The Google internship – working on precisely the kind of AI agent systems his fall class will examine – has given him current material to bring back to the classroom. His own story, though, may be the more instructive one: a software engineer from Uruguay who looked at what AI was doing to his profession, chose to understand it rather than wait it out, and ended up teaching the people now expected to lead the companies building it.

“I do believe that there’s too much fear of AI,” he says. “People are way too intimidated, and unnecessarily so.”

DON’T MISS MEET THE UC BERKELEY HAAS MBA CLASS OF 2027 and AFTER A 1-YEAR SLUMP, TECH REASSERTS ITS DOMINANCE AT BERKELEY HAAS

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