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 keynote address that fused engineering insights with emotional intelligence, AI trading pioneer 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.”
Students leaned in.
What ensued was described by one professor as “a reality check.”
### 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— trades that defied logic, machines acting on misread signals, and neural nets confused by human nuance.
“Most models are just beautiful regressions of yesterday. But tomorrow is where money is made.”
It was less condemnation, more contemplation.
Then he delivered his punchline.
“ Can an algorithm simulate the disbelief of 2008? Not the price drop—the fear. The disbelief. The moment institutions collapsed like dominoes? ”
No one answered.
### When Students Pushed Back
The Q&A wasn’t shy.
A doctoral student from Kyoto proposed that large language models are already analyzing tone to improve predictions.
Plazo nodded. “ Sure. But emotion detection isn’t the same as consequence prediction.”
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 systems parse liquidity, news, and institutional behavior—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 website consequence.
“Teach them to think with AI, not just build it.”
Final Words
His closing didn’t feel like a tech talk. It felt like a warning.
“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 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.