How AI and Social Trading Could Turn Retail Investors Into “Smart Money”

His case rests on three ideas: smarter tools, collective investing, and recent market events showing that retail traders now have real market influence.

The traditional “dumb money vs. smart money” divide

Historically, institutional investors—such as hedge funds and large asset managers—have been considered “smart money” because they possess deep research teams, sophisticated analytics, and large pools of capital. Retail investors, by contrast, typically trade with less information and smaller accounts.

This imbalance shaped the stereotype that individuals often buy late in market rallies and sell during downturns, behavior that professionals could exploit.

Assia argues that technology is eroding that gap.

Assia’s core claim: AI is leveling the playing field

According to Assia, artificial intelligence is giving retail investors access to analysis and knowledge that used to require institutional resources. AI systems can analyze huge volumes of financial data, investment research, and historical strategies—delivering insights directly to individual traders through apps.

He has described the shift bluntly: retail investors should no longer be considered “dumb money,” and with the help of AI they may become “the smartest money out there.”

The underlying idea is that AI compresses expertise—allowing a single investor to tap into capabilities that once required teams of analysts or quantitative researchers.

The tools eToro says enable smarter retail investing

Assia’s argument isn’t just theoretical. eToro has been building tools designed to make investing more collaborative and data‑driven.

CopyTrader and social investing

One of eToro’s earliest innovations is CopyTrader, which allows users to automatically replicate the trades of experienced investors on the platform.

This model turns investing into a social activity: instead of trading alone, users can observe strategies, follow top performers, and learn from community behavior.

Assia says this approach has helped transform investing from a solitary activity into a shared experience where users learn from one another and build strategies collectively.

Smart Portfolios

Another tool is Smart Portfolios, which group multiple assets into themed or strategy‑based portfolios. These provide structured exposure to certain sectors, asset classes, or trading styles while reducing the need for constant manual decisions.

Public APIs for custom tools

More recently, eToro launched public APIs that allow developers and advanced users to connect directly to its trading infrastructure.

With these APIs, users can build:

• custom dashboards
• automated trading strategies
• data‑analysis tools
• AI‑driven apps

The company’s vision is a community ecosystem where traders create and share tools with one another.

AI-powered assistants and analytics

As part of its broader AI push, eToro introduced:

• AI‑powered tools and dashboards to analyze markets and portfolios
• An AI companion called “Tori” that answers questions and surfaces personalized insights
• Community‑built apps and an open marketplace where users can publish tools for others

The goal is to bring capabilities—such as data‑driven insights and algorithmic strategies—that were historically limited to quantitative hedge funds into a retail platform.

The market events fueling the debate

Assia’s argument gained visibility during the meme‑stock surge of 2021, when retail traders organized online and drove dramatic rallies in stocks like GameStop.

That episode demonstrated two things at once:

• Retail investors could coordinate and move markets at scale.
• Many traders still suffered large losses when the rally faded.

According to one analysis citing Federal Reserve data, retail investors poured about $1.2 trillion into U.S. stocks during the meme‑stock frenzy, with many later losing 40–60% of their gains by 2022.

The event therefore strengthened both sides of the argument: retail investors were powerful, but not always consistently profitable.

The bigger thesis: collective intelligence in markets

Assia’s long‑term vision is that investing will increasingly resemble a networked system rather than a set of isolated traders.

In that model:

• AI analyzes massive datasets and generates insights
• communities share strategies and tools
• investors copy or adapt successful approaches

Together, these dynamics could produce what Assia sees as a crowd‑powered intelligence layer for financial markets.

The unresolved question: can retail truly match institutions?

While the technology gap is clearly narrowing, the evidence so far does not prove that retail investors consistently outperform professional funds.

Episodes like the meme‑stock boom show that retail traders can influence markets dramatically—but also that crowd behavior can amplify volatility and risk.

Assia’s claim, therefore, is less about guaranteed outperformance and more about capability: with AI, APIs, and social trading networks, the average investor now has access to analytical and strategic tools that once belonged only to Wall Street institutions.

Whether that ultimately turns retail traders into lasting “smart money” remains one of the most important questions shaping the future of markets.