Fogo Co-founder: AI First Helps Retail Investors Establish Discipline, Then Pushes Them Towards Systematic Trading Near High-Frequency Trading Levels
Fogo's co-founder pointed out in a public discussion that current high-frequency trading (HFT) firms are technically far ahead due to "co-location, ultra-low latency, and complex trading models," while retail investors lack systematic tools, creating a significant gap between the two. He believes that AI should first help retail investors address the fundamental bottlenecks of "discipline" and "risk management" before discussing subsequent capability upgrades.
The co-founder proposed that the first step is to let AI automatically manage risks: setting a rule that "when entering a position, one is only willing to bear a maximum loss of X amount," with AI strictly enforcing stop-loss and position adjustment rules to prevent retail investors from over-leveraging or refusing to exit under emotional pressure. This automated discipline can significantly reduce capital consumption, allowing retail investors to structurally approach the survival capabilities of "professional traders."
Next, as AI models gradually approach HFT-level complexity in signal generation, factor combinations, and real-time parameter adjustments, the gap between retail investors and institutions in terms of "model quality" and "decision chain" will be greatly compressed. At this stage, retail investors will no longer just be "tool users," but individuals with "almost HFT-equivalent model and strategy abstraction capabilities," although the underlying infrastructure (such as latency, data sources, and clearing channels) will still be difficult to equalize completely.
Source: Public Information
ABAB AI Insight
AI's "empowerment" of retail investors does not start with "directly defeating HFT" but begins with transforming "humans from unstable variables" into "systematic execution ends." In traditional structures, retail investors often fail not because of "lack of signals" but due to "execution loss of control," while HFT's comparative advantage lies in "stable, low-emotion, repeatable systems." AI's intervention at the "risk control" level essentially strips retail investors of their "human weaknesses" from the trading chain, allowing their capital loss and risk exposure to approach institutional levels.
On a deeper level, this drives the evolution of a "layered trading capability" structure: HFT controls "execution speed and market microstructure" at the lowest level, while at a slightly higher level, AI-driven systematic models are becoming the "mid-level" capability layer, allowing retail investors to access this layer through subscriptions or platforms, without needing to build colocated data centers themselves. This structure is similar to manufacturing: core components are monopolized by a few manufacturers, but the downstream "integration and application" segment is open, allowing many non-giant companies to offer competitively viable products.
However, it is important to note that AI's "approximation to HFT" is more in the dimensions of "strategy abstraction" and "risk management," rather than "physical layer competition." HFT's advantages in latency, data pathways, clearing, and counterparty relationships are still determined by infrastructure, capital scale, and licensing systems. AI's role for retail investors is to make "decision-making at macro and mid-frequency levels" and "approaching quantitative institutions' model complexity" more accessible, while still reserving "micro-second games" for institutional capital and dedicated networks.
Therefore, this time, "AI narrowing the gap" is essentially about "aligning with mid-to-upper level algorithms," rather than "restructuring game rules at the bottom level." In the long term, this will divide the market into three levels: "physical layer HFT, platform layer AI models, user layer executors," with retail investors embedded in the "platform layer," relying on AI model architectures rather than solely building their own systems. In this structure, the variables determining returns will shift from "whether one has low-latency infrastructure" to "whether one can access high-quality models and hedging combinations."