Retail traders have long faced an uphill battle. Markets are dominated by institutions with faster execution, richer data access, and privileged information that most retail traders can’t reach. Crypto promised to level the playing field via transparency, but professional quant firms and automated strategies have quickly claimed that advantage. Only a small fraction of day traders – between 3% and 20% by some estimates – actually manage to stay profitable.
That raises a compelling question: can artificial intelligence close the gap – or will it widen it?
AI in the Market
Artificial intelligence is already reshaping how markets operate. Platforms like Numerai, Trade Ideas, and Cryptohopper enable traders to analyze sentiment, automate strategies, and backtest models without writing code. But these tools are typically controlled by centralized companies. The models often work as a “black box” – users can’t always see how data is gathered, how decisions are made, or whether there is bias in the training or decision-making process.
With such opacity, AI could exacerbate existing unfairness rather than remedy it. Institutions with superior infrastructure and data will still likely have an edge.
Experimenting With Different AI Initiatives
Some projects are experimenting with more open and transparent AI systems. True Trading, in particular, aims to build an AI-native decentralized exchange (DEX) on a Solana Layer 2 network. Its system is designed to learn from on-chain (blockchain) data, with every trade and user interaction feeding back into the model.
Ben Bilski, co-founder of True Trading, told CCN that Solana’s liquidity, speed, and architecture make it uniquely suited for AI-native trading. He believes the “centre of liquidity” is where trading platforms should build, to align with where users already are.
True Trading’s design includes a chat-first interface: users can ask questions, get strategic or risk analysis, and execute trades in a single flow. The AI also aims to coach users – explaining leverage, exposure, and risk.
True Trading is currently raising funding to accelerate development of this model and its infrastructure.
Where This is Heading
The convergence of AI and blockchain may become transformative in finance. Open data and adaptive models could help detect manipulation, flag suspicious behavior, and improve efficiency for everyone – not just those with deep pockets.
Still, technology alone does not guarantee fairness. Key issues remain: model bias, access to computational resources, governance of the AI systems, regulatory oversight, and whether users really understand what decisions the AI is making.
The human role also remains essential. As Ben Bilski suggests, professionals are not going to be replaced but augmented. Trading strategy, judgment, and adapting to market shifts are tasks that AI cannot fully replicate.
In the end, the promise of fairness depends on how transparently AI is built and how fairly it is governed. If True Trading or similar initiatives succeed, retail traders could gain access to tools once available only to large institutions.
