Even before going through the massive boom over these past couple of years, artificial intelligence has been presented as the technology that would transform everything, including financial markets. But when we look at the results, most of the time, the adoption of AI only led to minor improvements: things like faster customer support or better analytics. Useful, certainly, but not quite “revolutionary” enough to match the promise.
2026, however, feels like a new stage in that regard. If before that the market had only seen what were essentially upgrades to previously-existing methods, then now we have cases like Coinbase’s AI-powered investment advisors capable of executing trades autonomously and Kalshi’s ‘Harrison’ agent that helps design and stress-test the prediction market contracts.
Taken together, these developments point to a broader shift, where AI is finally becoming part of the trading infrastructure itself. It is no longer being treated simply as an add-on, meant to provide surface-level enhancements while ultimately staying separate.
As this change gradually takes place, it brings with it a very timely question: what does it actually mean for a brokerage to become truly “AI-native?”
AI as the new operating layer
Financial markets have always generated more information than any individual human trader could realistically process.
Price movements are influenced by countless factors: macroeconomic data, company announcements, geopolitical events, social sentiment, blockchain activity, etc. — all of which need tracking if you want to stay up-to-date.
Even experienced professionals spend as much, if not more, time filtering information as they do making decisions.
Earlier generations of AI weren't particularly good at solving this problem. They could automate individual tasks, but connecting different types of information into something genuinely useful was beyond them.
That said, like it inevitably happens with any technology, AI models moved forward and became more sophisticated over time. With better data pipelines and autonomous agents in play today, these systems are genuinely capable of not only compiling enormous amounts of information in real time, but also acting on it.
Artificial intelligence no longer simply presents more data to the trader. It can help organize that data, explain relationships between disjointed market events, pull out insights that would otherwise remain buried, and even make deals happen on behalf of the trader.
Of course, that doesn't make these models infallible, but it certainly does add a whole new level of practicality and usefulness, compared to many of the AI tools that financial institutions experimented with even just a year ago.
AI-Native means changing workflows, not adding features
I’ve seen quite a few firms that described themselves as AI-powered because they've added a chatbot or an automated assistant somewhere in their product, but, in my eyes, at least, that’s not quite enough to truly call oneself “AI-native.” Why? Because such additions don't fundamentally do anything to change how traders work.
A real AI-native brokerage approaches the problem differently: by embedding intelligence directly into the trading environment itself, so as to actively help its users interpret the market as they make decisions.
This matters a lot more than some might think, seeing as trading today has become less about accessing information and more about understanding it.
Market data is widely available, but the real challenge lies in identifying which signals deserve attention and which can (and should) be ignored.
Trust is the biggest challenge
I’ll admit, this is where discussions around AI can often be overly optimistic.
Financial services operate in a very strict environment, where mistakes can be more than a little costly. Recommendations generated by an AI model influence trader choices involving substantial amounts of capital, which is why they should be approached cautiously and responsibly.
Unfortunately, even now, despite all the advancements, LLMs remain susceptible to hallucinations and false outputs. Market conditions change all the time, and because of that, models trained on historical data can struggle during periods of serious volatility, when everything keeps shifting very quickly.
As such, reliability and governance come to the forefront of considerations. Who validates the outputs? How are recommendations tested? How can firms explain why their AI made that particular suggestion?
These questions are increasingly important both to regular users and to regulators, whose attention towards AI grows sharper every day. Frameworks like the EU AI Act are pushing financial institutions to think about the explainability of models, risk management, and human oversight. Introducing AI into investment workflows cannot come at the expense of accountability, that much has become abundantly clear by now.
In terms of becoming AI-native, this means that the task that lies before brokerages is not just technological in nature, but also organizational. Engineering teams, compliance officers, risk managers, and business leaders all need to take part in figuring out how AI systems can be integrated into their workflows in a manner that’s auditable and trustworthy.
That process inevitably takes a longer time to work out, but it also stands to create far more sustainable products and long-lasting trust with the rest of the market. And so it’s worth the effort.
Combining intelligence with responsibility is the future
Personally, I don't believe that AI will ever fully replace traders — just as electronic trading never eliminated the need for human expertise. What will happen instead, I think, is that we'll see a shift in what human expertise is meant to look like.
As basic information gathering becomes increasingly automated through AI, human professionals can spend more time actually evaluating scenarios and exercising their own judgment. The competitive advantage will, in turn, come from asking better questions and making better decisions with the information that’s equally available to everyone.
This is ultimately why I believe AI-native brokerages will become the industry standard. Financial markets have reached a level of complexity where simply providing access to markets is no longer enough. Helping users understand what they're seeing is the name of the game now.
Creating systems where artificial intelligence enhances human decision-making without taking away the responsibility that comes with it — that’s the future of brokerage. Firms that can do that, while also remaining transparent and acknowledging the limitations of what AI can and cannot do, stand a good chance of leading in this next stage.
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