What the Machines Still Can't Do: Joseph Plazo’s Hard Truths for the Next Generation of Investors on the Boundaries of Artificial Intelligence
What the Machines Still Can't Do: Joseph Plazo’s Hard Truths for the Next Generation of Investors on the Boundaries of Artificial Intelligence
Blog Article
In a rare keynote that blended technical acumen with philosophical depth, financial technologist Joseph Plazo issued a warning to Asia’s brightest minds: the future still belongs to humans who can think.
MANILA — What followed wasn’t thunderous, but resonant—it reflected a deep, perhaps uneasy, resonance. At the packed University of the Philippines auditorium, future leaders from NUS, Kyoto, HKUST and AIM expected a triumphant ode to AI’s dominance in finance.
But they left with something deeper: a challenge.
Joseph Plazo, the architect behind high-accuracy trading machines, chose not to pitch another product. Instead, he opened with a paradox:
“AI can beat the market. But only if you teach it when not to try.”
The crowd stiffened.
What followed wasn’t evangelism. It was inquiry.
### Machines Without Meaning
His talk unraveled a common misconception: that data-driven machines can foresee financial futures alone.
He presented visual case studies of trading bots gone wrong—algorithms buying into crashes, bots shorting bull runs, systems misreading sarcasm as market optimism.
“ Most of what we call AI is trained on yesterday. But tomorrow is where money is made.”
It was less condemnation, more contemplation.
Then he paused, looked around, and asked:
“Can your AI model 2008 panic? Not the price drop—the fear. The disbelief. The moment institutions collapsed like dominoes? ”
And no one needed to.
### When Students Pushed Back
The Q&A wasn’t shy.
A doctoral student from Kyoto proposed that large language models are already detecting sentiment and adjusting forecasts.
Plazo nodded. “ Yes. But knowing someone is angry doesn’t mean you know what they’ll do. ”
Another student from HKUST asked if real-time data and news could eventually simulate conviction.
Plazo replied:
“You can model lightning. But you don’t know when or where it’ll strike. Conviction isn’t math. It’s a stance.”
### The Tools—and the Trap
His concern wasn’t with AI’s power—but our dependence on it.
He described traders who surrendered their judgment to the machine.
“This is not evolution. It’s abdication.”
But he clarified: he’s not anti-AI.
His firm uses sophisticated neural networks—with rigorous human validation.
“The most dangerous phrase of the next decade,” he warned, “will be: ‘The model told me to do it.’”
### Asia’s Crossroads
In Asia—where AI is lionized—Plazo’s tone was a jolt.
“Automation here is almost sacred,” noted Dr. Anton Leung, AI ethicist. “Plazo reminded us that even intelligence needs wisdom.”
In a follow-up faculty roundtable, Plazo urged for AI literacy—not just in code, but in consequence.
“Teach them to think with AI, not just build it.”
Final Words
The ending wasn’t applause bait. It was a challenge.
“The market,” Plazo said, “isn’t just numbers. It’s a story. And if your AI doesn’t read character, it won’t understand the story.”
No check here one clapped right away.
The applause, when it came, was subdued.
Another said it reminded them of Steve Jobs at Stanford.
He didn’t market a machine.
And for those who came to worship at the altar of AI,
it was the lecture that questioned their faith.