SmartX founder David said in a PANews interview on Oct. 27 that AI is set to move crypto trading from a search era to a recommendation era, but he argued that the technology should remain a support tool rather than the final decision-maker. For retail traders, he said, the real edge is not chasing one outsized win. It is staying alive long enough to keep playing.
David described a roughly six-year path in crypto. He said he first encountered digital assets around late 2019 and early 2020 while studying at Cambridge, after friends introduced him to cryptocurrencies including Ethereum and several major public blockchains at the time. He then spent about a full year learning the market. He started as an individual investor, took part in The Sandbox and DeFi in 2020, moved into NFTs in 2021, and at one point joined AppCoin DAO under Yuga Labs on a part-time basis to work on metaverse-related business.
He later shifted toward more professional trading. According to David, he built a team for proprietary quantitative trading from 2022 to 2023. By the end of last year, he had started thinking about a consumer-facing social trading terminal. SmartX development began this year, and the project has received investment from Yzi Labs.
An 80/20 trading framework centered on blue chips
David said outside discussion about his Binance live trading ranking often misses that the leaderboard is dynamic. On a 365-day basis, he said he once reached Top 3 and has generally stayed near the front. More recently, a larger drawdown pushed his ranking lower, though he said he still often returns to the Top 5 over shorter periods.
He described himself as a medium- to long-term swing trader. About 80% of his trading exposure is concentrated in blue-chip assets including BTC, ETH, BNB, SOL, HYPE, and UNI, while roughly 20% is allocated to smaller altcoins. He added that his blue-chip book is close to $10 million and that a paper drawdown of about $2 million over the past few days was, in his view, still within a normal range of fluctuation.
Using the current Bitcoin cycle as an example, David said he opened both spot and futures long positions when BTC was above $60,000. When the price moved into the $70,000 to $80,000 range, he gradually closed most of the futures exposure and locked in profits from that leg. After the recent pullback, he said he reopened positions and began trading a second swing. Since last year, he has also traded tokenized U.S. equities onchain, including large-cap technology names such as Nvidia and Apple.
Why he moved from trading for himself to building a product
David said AI has changed dramatically over the past decade, while trading products have seen little meaningful innovation over the same period. He called that mismatch unreasonable, especially given that trading platforms serve users on a billion-scale globally. In his view, two pain points stand out.
The first is that most trading products look the same for everyone. A user with $5,000 and a user with $5 million often open the same app and see the same interface and the same content, even though their needs are fundamentally different. One may be trying to multiply a small base of capital and is willing to take high risk in volatile assets. The other is more likely to seek steadier returns and cannot tolerate the possibility of losing principal. David said those users have different risk preferences, different return targets, and different asset-selection logic, yet they are still fed the same information.
That is why he believes trading products will eventually move toward highly personalized recommendations. He compared the shift to content feeds on Douyin and X, where each user sees a different stream. In his view, the same should happen in trading software, where the assets, signals, and strategies shown to each user are tailored rather than uniform.
The second pain point is what he called the unmet demand for outsourced trust. Many users like copy trading or joining alpha groups to learn from others, but what they see may not be real. David said some group operators post screenshots of profits from demo accounts while their real positions may be only a few hundred dollars. Their actual business, he said, is referral commissions rather than trading profits, while uninformed users are drawn in by the appearance of success.
He argued that people are naturally inclined to outsource trust because trading is complex. Choosing from thousands of assets and making repeated decisions consumes energy. For many users, it is easier to decide whether another person seems trustworthy than to do the full analytical work themselves. He said the market’s fascination with Warren Buffett’s holdings reflects the same instinct.
The problem, he added, is that trading data on centralized exchanges is not transparent enough for users to verify a trader’s real record. SmartX is trying to combine the smooth experience of a centralized exchange with the transparency of a decentralized one, using verifiable onchain trading data to better serve that demand for outsourced trust. David said this was one of the main reasons he moved from trading for himself to building a product.
AI can help filter noise, but users must keep control
David said AI’s biggest role in trading is to help users understand themselves and build a trader profile around preferred asset classes, holding habits, and historical behavior. Rather than expecting AI to make users profitable on its own, he said it should first reduce the frequency of mistakes.
At this stage, he sees AI as a tool for building a customized information feed. In an onchain market flooded with data, AI can filter noise and surface assets that better match a user’s capital base and risk tolerance. That can help users make decisions within what he called their own comfort zone.
Still, he drew a hard line on decision-making authority. AI may assist, he said, but the final call has to remain with the user. His reasoning is straightforward: markets are a zero-sum game. If everyone relies on AI and those systems converge on similar strategies, alpha disappears and no one is left with an edge.
Copy trading starts with style matching, not profit screenshots
Asked about information overload onchain, noise on X, Telegram alert groups, and the growing number of tactics aimed at trapping copy-trading capital, David said users should stick to two principles.
First, prioritize blue-chip allocation. Large-cap, liquid assets such as BTC and ETH are harder for small groups of traders to manipulate. Smaller altcoins and meme tokens usually have thinner liquidity, and he said even the capital of a few dozen people can move prices enough to make them vulnerable to reverse operations targeting copy traders.
Second, match style before deciding to copy. Users should try to understand another trader’s logic, holding period, and risk preference before following any position. The key question is whether that style fits their own. David summed it up with a line he used in the interview: copying homework is not just about copying the answer; you also need to understand how the problem was solved.
The biggest opportunity and the biggest risk in AI-driven trading
Looking further out, David said the biggest opportunity for ordinary investors is that onchain trading could shift from a search era to a recommendation era. Instead of stitching together tools, hunting for information, and manually screening assets, users may find it easier to access signals and strategies that fit their own circle of competence.
The biggest risk, in his view, is overconfidence amplified by better tools. In the past, users often hesitated before entering an unfamiliar market. Now, when AI produces an analysis that appears rigorous and coherent, users can quickly fall into the illusion that they already understand the trade. That, he said, may push them toward higher leverage and larger positions.
He stressed that a correct analysis and a profitable trade are not the same thing. Between knowing and doing, he said, lies the full force of market volatility and human nature. Better tools do not reduce the difficulty of trading. In some cases, they can magnify misjudgment.
SmartX is not planning a token now and expects fees to drive revenue
SmartX is still in its alpha stage and has introduced growth mechanisms such as an ambassador program. That naturally leads the market to speculate about a token or points system. David said clearly that the product is still too early for that discussion and that SmartX will not issue a token simply for the sake of issuing one.
He said he often uses stocks as an analogy for tokens. The legal definitions are different, but in terms of value logic, he wants any future token to have stock-like characteristics: as the product grows and its ability to capture value improves, the token’s value should rise alongside it, creating a positive flywheel between the product and the token.
On monetization, David said SmartX’s revenue pillar is trading fees. He argued that many people underestimate the size of the trading business, which he described as a market worth at least tens of billions of dollars. In his view, the ceiling is not low, and the viability of fee-based models has already been proven by many centralized exchanges and DeFi trading protocols.
He added that SmartX does not rely on subscriptions or token economics for its business model. The idea is simpler: improve the product experience first, make trading easier and more personalized, and make the interface feel as smooth as consuming content. User growth and revenue growth should follow from that.
David also acknowledged that retention in trading products is affected by bull and bear cycles. His answer is to shift product priorities with the market. In bull markets, the team will focus more on refining the trading experience. In bear markets, it may strengthen social features and use community and content to retain users through the cycle.
Why he is watching cultural and IP assets
David said alternative assets have performed well in recent years and that he plans to focus more on cultural and intellectual property-related assets in the next phase.
He argued that many investors treat cash flow as the only source of value, but scarcity, emotional demand, and long-term cultural identity can also support asset prices over time. As an example, he said Pokémon cards and other trading card game collectibles have outperformed Bitcoin over the past decade, with some rare cards posting especially striking price gains.
He extended the same logic to Web2 content. AI has made it much easier to write fiction and produce video, he said, and Hongguo short dramas have expanded rapidly in recent years. The platform is filled with AI-generated videos and still attracts broad interest. To him, that reflects emotional needs that many ordinary people do not satisfy in real life, including familiar narratives such as an underdog rising to power.
From that perspective, he believes AI-created cultural or IP assets may also be assigned value in the future. He said culture and IP could support a trading market lasting for decades, producing assets backed by strong consensus and strong IP, with the ability to hold long-term allocation value across cycles.
Five rules for retail traders
David closed the interview with a set of practical rules for ordinary traders. His core point was simple: trading is not a contest to see who makes the most in one shot. It is a contest to see who lasts.
- First, do not go all in. Keep enough principal. In his words, if you do not lose everything, you still have a chance to win.
- Second, understand leverage correctly and do not let the headline number fool you. He said leverage risk cannot be judged by the multiple alone. It has to be assessed in the context of notional exposure, margin ratio, and the size of the overall account. Position management matters more than the leverage label itself.
- Third, define drawdown standards. Before buying any crypto asset, he said traders should ask whether they can tolerate a drawdown of about 75%. He described that as a normal decline in Bitcoin’s four-year cycle. If the answer is no, then they should not buy. If a position falls more than 75%, the next step is to judge whether the fundamentals have changed in a fundamental way. David said some of his own holdings have gone through drawdowns of about 80%, and if he concluded that fundamentals had not deteriorated and that the move was driven by macro conditions or sentiment, he would add on the way down. If the drawdown reaches 90%, he said, it is time to cut the loss.
- Fourth, build a position structure. David said roughly 70% to 80% of his holdings are in stablecoins and blue-chip assets, with the rest reserved for flexible trading. Altcoins and meme tokens make up only a limited share. He does not chase 10x or 20x returns on a single coin. For most people, he said, a 50% return on one trade is already very good.
- Fifth, build a trading system. David said he is not a technical-analysis trader. He watches only two indicators in crypto: price and open interest, or OI. When trading U.S. equities, he said he does not even look at OI and focuses only on whether the asset is expensive or cheap at the current price. He compared it to shopping: you need an internal yardstick for what something is worth, then buy when it is cheap and sell when it is expensive.
David added that all of these comments reflect his personal views and do not constitute investment advice.
