Trading education has long sat in an uncomfortable space between finance, online content, and personal development. Promises of fast profits and lifestyle freedom dominate much of the market, while structure, learning science, and accountability are often left behind.
As fintech and edtech increasingly converge, a new generation of platforms is attempting to professionalise how complex financial skills are taught. The Trading Cafe is one of the more ambitious examples of this shift. Built around structured learning, rigorous feedback loops, and a clear separation between students and live traders, the platform aims to redefine what responsible, scalable trading education can look like.
In this long interview with Peter Visser, Co-Founder of The Trading Cafe, we go over why most trading education fails, how learning design matters as much as market expertise, and where technology, including AI, can genuinely improve outcomes without creating new risks.
Tell us about The Trading Cafe. What gap did you identify in the trading and financial education ecosystem, and how does your platform aim to address it?
The biggest gap we identified was the lack of proper, structured education in trading. The industry is full of messaging around making money, gaining freedom, and showing off lifestyle perks. And sure, that sounds appealing. But it creates a huge misalignment with what it actually takes to become a professional trader. Trading is a skill like any other. It’s no different from becoming an electrician, a doctor, a pilot, or a plumber. Those are skills that take time, structure, and deliberate practice to learn. Trading should be treated the same way, but it isn’t. Instead, it’s often sold as something you can master with a few YouTube videos or a short online course.
What was missing was genuine education based on proven learning principles.
How should a module be structured? How do people actually practice and how do they receive feedback and improve over time? That was the first real gap we saw in the market. Proper education, done seriously.
Education not delivered well
The second gap was the way education is usually delivered. A lot of it comes from individual gurus teaching their own way of doing things. Even when they are good traders, the education itself is often weak. What we did differently was bring multiple professional traders together. Right now, we have five, and they meet every single week. The focus of those meetings is very simple: how do we help students get results faster?
On top of that, we combine their subject matter expertise with actual education specialists. We have around three education experts in those meetings as well. It becomes a collaborative process between people who deeply understand trading and people who deeply understand how humans learn. They meet every week, refine the material, and improve the process. At that point, it stops being a guru model and starts functioning like a real institution.
The trading education market is often criticised for being fragmented and unregulated. From your perspective, what are the structural issues holding the sector back?
That criticism is fair. The sector really is unstructured and unregulated, and a big reason for that is simply because it’s global. No single country has true jurisdiction over trading and financial education. Anyone can set up a website in pretty much any country and start teaching people online, which makes oversight extremely difficult.
Structurally, the biggest issues are fragmentation and the role of social media algorithms. Platforms like YouTube, Instagram, TikTok, and Facebook don’t reward usefulness or real progress. They reward attention. How many people clicked, how long they watched, how entertaining or controversial something was. That creates an environment where entertainment and clickbait outperform actual education. If you understand storytelling, hype, and marketing, you can do very well, even if you don’t really understand trading or education at a deep level.
Because of that, you end up with a lot of people who are great at grabbing attention but weak on financial expertise and learning design. Proper education requires marrying subject matter expertise with educational principles, and that’s expensive. If you actually want to do this right, the investment is significant. The infrastructure we’ve built took a lot of time, money, and effort. The barrier to entry for doing things properly is high, so most people simply don’t bother.
Odd results
The result is that the entire industry ends up with a scam-like reputation by default. When someone says they teach Forex or stock trading, the baseline assumption is often that it’s a scam. That lack of structure and credibility has eroded trust and status in financial trading education overall.
We’re actively trying to change that. We’re working toward proper licensing, setting up our hedge fund through a separate entity in the UK, and building something that operates at a much higher standard. The goal is to help set a new bar for how trading education should look. The structural issues are big, and it’s not an easy thing to tackle, but that’s exactly why it matters.
You’ve spoken previously about separating genuine trading expertise from effective teaching. Why is that distinction so important, and how have you built it into your model?
It’s absolutely critical. Without that distinction, you don’t really have anything. You need genuine subject matter expertise, that part is obvious and everyone agrees on that. If you’re teaching trading, you need real traders. But you also need expertise in education and teaching, and that’s a completely separate skill set.
Trading the financial markets is one skill. Teaching someone how to do it effectively is another. They don’t naturally overlap. You very rarely find truly skilled financial professionals who also deeply understand how people learn. Education is its own discipline. People study it, get degrees in it, build careers around it, and spend decades refining how knowledge is transferred. Imparting skill and understanding to someone else isn’t simple, even though it’s often treated that way in this industry.
What we did was deliberately separate those two roles and then bring them together in a structured way. We have professional traders focused on what actually works in the markets, and education experts focused on how to turn that knowledge into something people can genuinely learn, practice, and improve at. When you marry those two worlds, trading expertise and education expertise, the outcome is better student results. That’s really the point.
Because of that focus, we see a level of student success that I honestly haven’t seen anywhere else online. And it comes directly from respecting that distinction instead of assuming that being good at something automatically means you can teach it well.
Many fintech platforms focus on content delivery. You’ve placed equal emphasis on learning structure, feedback, and live interaction. Why was that architectural choice critical?
Because we focus on what actually works. Every part of what we’ve built is backed by research in education and learning science. The question we start with is simple but demanding: how do you help someone acquire a complex skill as quickly and efficiently as possible, in a way that actually sticks long term?
That thinking drives how our content and curriculum are designed. We’re not just delivering information. Each module follows a clear structure, usually nine to ten parts, that’s designed to teach people what to do, not just what to know. There’s a big difference between understanding a concept and being able to apply it under pressure, and the structure has to account for that.
The same applies to practice and feedback. Practice isn’t random, and feedback isn’t optional. We’re very intentional about how people practice, what kind of projects they work on, and how feedback is delivered. We’ve built five different feedback mechanisms because one form of feedback is never enough on its own.
Live interaction plays a critical role in that system. It exists to remove friction, address blind spots, and deal with the barriers people inevitably hit while learning. It’s where gaps get filled and misunderstandings get corrected before they become habits.
Everything is grounded in evidence-based learning. Concepts like deliberate practice and metacognition aren’t buzzwords for us, they’re built into the architecture. We don’t add features just to add content or keep people busy. If something doesn’t contribute to learning faster and better, it doesn’t belong there. The goal is to get people competent in the shortest time possible, with results that actually last.
Financial education is often viewed as high-risk for consumers. How do you think platforms can balance accessibility with responsibility, particularly in trading?
First of all, it is high risk. There’s no way around that. When financial education is done poorly or irresponsibly, it can lead to real harm. People can lose significant amounts of money, even entire retirement accounts, because they were given incomplete or misleading guidance. That means risk management in education isn’t optional. It has to be actively managed.
The key question is how you make education accessible without causing harm. For us, it starts with redefining how people are labelled. Watching a YouTube video, consuming content, or placing a trade does not make someone a trader. It makes them a student. And that distinction matters a lot.
One big problem
One of the biggest problems in the industry is that people are treated as traders far too early. They’re encouraged to trade before they’ve built the skills, discipline, or psychological resilience required. We take the opposite approach. People are students of the market first. Only after they meet clear, objective criteria do they begin a guided transition into live trading.
It’s very similar to other professions. You don’t become a doctor and start operating immediately. You study, you train, you go through supervised practice, and only then do you earn the right to operate independently. Trading should follow the same model.
In our case, students need to backtest at least 300 trades. They then need to paper trade or demo trade consistently for three to four months, focusing not just on execution but on psychology. Only after that do they move to live markets, and even then, it’s with a maximum of 10 percent of their disposable investment capital. That’s money they can afford to lose. The goal at that stage is simply to prove they can replicate their backtesting and demo results. If they can’t, they go back to being a student.
Student vs trader
I think the industry as a whole needs clearer definitions around what a student is and what a trader is. Ideally, those terms would carry real weight, similar to regulated professions. Even brokerages could play a role by recognising “student mode” versus live trading readiness. It wouldn’t be easy to implement, but ethically, it’s one of the most effective ways to reduce harm and stop people from unknowingly turning trading into gambling.
We can also see this problem show up very clearly with AI and generic advice. If you go to ChatGPT or pretty much any AI and ask how to learn to trade, the answer is almost always something like: read a few books, take a course, then start demo trading. And that advice is actually wrong.
Demo trading already puts someone into a transition or trader phase. At that point, you’re practicing execution, not learning the foundations of the skill. We’ve found that demo trading is actually one of the worst ways to learn how to trade if it’s introduced too early. There’s a lot of science behind why that is, around skill acquisition, feedback loops, and cognitive load, but it’s probably too much to unpack fully here.
The important point is that most guidance completely skips the true student phase. People are pushed into simulated trading environments before they’ve built the underlying decision-making framework. That reinforces bad habits instead of good ones. It’s another example of why the industry needs clearer definitions and a more responsible learning pathway, rather than jumping straight from content consumption into something that looks like trading but isn’t appropriate yet.
The fintech and edtech sectors are evolving rapidly. What technological shifts do you believe will have the biggest impact on how people learn complex financial skills over the next few years?
For us, the answer is actually very clear, even though it’s a bit counterintuitive. It’s AI. But not in the way most people think about it.
AI has become such an overused buzzword, mostly because it’s being positioned as a shortcut. People want instant answers. Is this a good trade? Is this a good investment? Just tell me what to do. In an industry that’s complex and constantly evolving, that approach doesn’t help people in the long run. It removes thinking instead of developing it.
The real opportunity with AI is in education, specifically in reducing the time it takes to get feedback. Almost every problem we face ultimately comes back to education. Better financial outcomes require better financial literacy, and that requires better learning systems.
Historically, one of the biggest bottlenecks in learning has been feedback. You hit a barrier, you ask a question, and then you wait. At university, you might wait days or even weeks to get clarity from a lecturer. That delay slows everything down. What AI allows us to do, when used properly, is remove that friction.
What we’ve built is an AI system trained on three years’ worth of live sessions. We’re talking thousands of hours of professional traders and instructors answering questions, reviewing projects, and giving real feedback. We’ve also fed in our full curriculum. That means when someone hits a barrier, they can ask a question and get context-specific guidance immediately, instead of waiting days.
Removing barriers
The result is that barriers get removed faster. When that happens, people upskill faster, move through curricula more efficiently, and reach competence sooner. That’s the real leverage point.
This only works if the AI is trained on specialised knowledge and grounded in sound educational principles. General-purpose language models aren’t enough on their own. The goal isn’t to outsource thinking. It’s to accelerate learning.
A lot of what we’re seeing right now with AI is about dumbing people down. Write my essay. Tell me what to buy or sell. Do the thinking for me. That leads to commoditisation. You become someone who just follows instructions. We believe the real power of this technology is the opposite. It’s about using AI to build stronger foundations, develop better judgment, and help humans learn faster and better than ever before.
AI is increasingly being used across financial services and education. Where do you see the real opportunities for AI in trading education, and where do you see the risks?
This really builds directly on the previous point. The real opportunity for AI in trading education is using it to remove barriers in the learning process. That’s the core use case. When people get stuck, when they don’t understand something, or when they need feedback, AI can dramatically reduce the time it takes to get past those obstacles.
Used properly, that leads to people becoming more skilled and more knowledgeable, not just more dependent on tools. That’s where the real value lies. The people who use AI well, and this is already happening, are using it to accelerate skill acquisition. They’re removing friction from learning the things they need in order to achieve real outcomes.
The risk is when AI is used as a shortcut instead of a support system. If it’s positioned as something that makes decisions for you, rather than helping you understand how to make better decisions yourself, it actively undermines learning. In trading especially, that’s dangerous. It creates false confidence without competence.
So in simple terms, the opportunity is in upskilling humans faster and more effectively. The risk is in replacing thinking altogether. How AI is positioned and used will determine which of those two paths people end up on.
You’ve taken a cautious stance on AI-generated trading advice. Why do you believe overreliance on AI could be problematic in this domain?
AI-generated trading advice is problematic because, in practice, it’s often wrong or massively oversimplified. We see this firsthand. Through our infrastructure, we work with a very large sample of traders. Right now, we have around 2,500 active students in our paid trading academy and roughly 70,000 people in our free The Trading Cafe community. That gives us a broad, real-world view of where people struggle and what they’ve already tried.
On top of that, we’ve collected over 5,000 detailed questionnaires where people explain their challenges, their past attempts, and what hasn’t worked for them. A large number of them have relied on AI for guidance, and the feedback is consistent. The advice they received was poor. It led them in the wrong direction or gave them a false sense of simplicity around what is actually a very complex skill.
What works vs not works
We also know, very clearly, what works. We track successful students end to end. For us, success means verified, live traders using their own capital, with journals, screenshots, and data we can review. When we look at the paths those people took to become consistently profitable, AI advice is not part of it.
Generic AI guidance on trading tends to oversimplify the process and flatten nuance. If you ask how to learn to trade profitably, you’ll often get surface-level steps and even recommendations for content that doesn’t remotely reflect what’s required to develop real skill. That can send people down the wrong path for months or even years.
The core issue is noise. Learning trading requires the removal of noise, not the addition of more of it. For AI to be useful in this domain, it has to be trained on very narrow, highly specific data and grounded in a proven learning process. Without that, overreliance on AI doesn’t accelerate progress, it delays it.
At the same time, you’re actively using AI within your own platform. Can you explain how you approach AI as an augmentation tool rather than a replacement for human expertise?
For us, the distinction comes down to what the AI is trained on and what role it’s allowed to play. We’ve built a very specific system, and we worked with a specialist company to do it properly. The AI is trained exclusively on real, live interactions between our instructors and our students over the past three years, and we continue to update it with new sessions as they happen.
We run around 30 live sessions every week. These include Q&A sessions, support sessions, legacy sessions, and project reviews. Students bring very specific problems, questions about modules, individual trades, market conditions, and psychology. Over the course of a year, that adds up to thousands of hours of expert-led problem solving. And importantly, every one of those questions was answered by a real professional, not by an algorithm guessing.
That dataset alone covers the vast majority of issues students will ever encounter. On top of that, we’ve fed the AI our entire curriculum. Every step, every practice project, and every requirement needed to move from complete beginner to verified, live trader is in that system. It isn’t trained on generic market content or broad internet data. It’s trained on our process, and nothing else.
Structured and constrained
Because of that, the AI is very structured and very constrained. It doesn’t present endless options or opinions. When a student asks a question, it gives a clear answer and then points directly to the source. That includes the exact lesson or live session where the topic was covered, with a timestamp, so the student can immediately watch the context behind the answer.
That’s how we use AI as augmentation. It removes friction, shortens feedback loops, and helps people move forward faster, but it never replaces human expertise. AI doesn’t trade for you, it doesn’t make decisions on your behalf, and it doesn’t bypass the learning process. It supports skill development rather than trying to automate it.
Looking back on your journey so far, was there a particular moment that validated the direction you were taking with The Trading Cafe?
It’s never really one single moment. It’s usually a series of small confirmations that slowly stack up until you realise this is actually working. But there are two moments that really stand out for me.
The first one came from looking at student results. We track everything. Every student moves through clear phases: the learning phase, backtesting, demo trading, and then live trading. All of that is monitored, and if someone is struggling, we usually step in before they even ask for help. It took time for people to reach live trading, but once they did, something really interesting started happening.
We began seeing students produce incredibly strong live results with their own money. What surprised me most was that, in many cases, their risk management was even tighter than our instructors’. Their drawdowns were extremely low because they followed the process so precisely. They weren’t reckless, and they weren’t emotional. They’d been trained to be disciplined, almost robotic in execution, while still making nuanced decisions. Seeing that list of successful live traders grow was a huge validation moment.
Hitting home
That really hit home during our first prize-giving. We ran an all-hands webinar, invited the entire community, and awarded prizes to students who had achieved the best verified live trading results. This was real money, real accounts, real data. That was around mid-2025, and honestly, seeing the statistics laid out like that was shocking in the best possible way. Now, as we’re moving into 2026, that group just keeps growing, and that’s when I thought, okay, this actually works at scale.
The second moment came from somewhere completely different: Reddit. We got our first proper Reddit post about six or seven months ago, where someone wrote a long, detailed review of The Trading Cafe and the Trading Academy. It was overwhelmingly positive. What made it even more meaningful was the response. Reddit is naturally sceptical, and the comments underneath, maybe ten to twenty of them, were also largely positive.
Since then, there’s been this ongoing stream of people on Reddit recommending us to others. That kind of organic validation, in an environment that’s usually hostile to anything trading-related, really made it sink in. That was the moment I realised this thing has legs, and we’re genuinely building something that people trust.
Community trust appears to be a key part of your growth. How important is external validation in a sector where credibility is often questioned?
External validation is extremely important, especially in an industry where trust is already low. I’ll use Reddit as a very clear example of why this matters so much.
Today, the way people research companies has changed. Google’s AI, ChatGPT, Grok, and other search-driven AI systems all pull from external sources, and Reddit is a major one. It’s not the only source they use, but it carries a lot of weight because it’s seen as unfiltered, public opinion.
To put that into context, we have around 1,100 to 1,200 Trustpilot reviews collected over several years. Roughly 90 percent are five-star, about 7 percent are four-star, and only a small percentage fall below that. People can read both positive feedback and criticisms, and all of those reviews are verified. Trustpilot confirms that the reviewers are real customers.
What’s interesting is how AI systems interpret this. They’ll look at that dataset and conclude that the company is generally trusted. But then they’ll also reference a handful of Reddit comments, sometimes even a single post, and give it comparable or even greater weighting. You’ll often see something like, “Here’s what these few people said on Reddit,” placed alongside years of verified reviews.
That makes external validation on platforms like Reddit incredibly powerful. In a sector where credibility is constantly questioned, those independent, organic conversations can heavily influence perception. Anyone doing serious research on us today, especially using AI-driven search tools, will encounter that external validation very quickly. And that has had a significant impact on how we’re perceived and how we’ve grown.
Your platform blends fintech, edtech, and community-driven learning. How do you think this convergence will shape future financial education models?
I think it really depends on how successful this model ultimately becomes and how large it scales. If we stayed small and relatively unnoticed, it probably wouldn’t move the needle for the industry. But that doesn’t seem to be the direction things are going. There’s already a lot of attention being paid to what we’re doing.
For this kind of convergence to genuinely shape future financial education models, it needs to be proven at a global level. And even then, it’s not an easy model to copy. What we’ve built pulls expertise from multiple disciplines, not just trading. We combine professional traders, education specialists, technology, psychology, and even health and fitness. That last part surprises people, but physical and mental health have a huge impact on trading performance. The tools and technology traders use also directly affect their outcomes. All of these elements are critical, and they only work because they’re integrated properly.
If, a few years from now, we’re consistently producing hundreds of profitable traders and those traders start getting picked up by hedge funds, that’s when the story really changes. At that point, people will try to replicate the model. I wouldn’t be surprised if acquisition conversations start happening as well, simply because institutions are always looking for reliable talent pipelines.
But before any of that, we still have a lot of work to do. This model needs continued proof and scale. It’s difficult, it’s resource-intensive, and there are no shortcuts. That’s exactly why it works, and also why it’s hard for others to replicate.
Which thinkers or technology leaders have influenced how you’ve approached product design, learning outcomes, and long-term platform strategy?
If you really break down what this business is at its core, it’s about taking people who are genuinely excellent at what they do and then coaching them in a direction that actually works together. From that perspective, John Wooden has probably had the biggest philosophical influence on how we think about the company, especially through the lens of leadership and team structure. My co-founder, Zack, had everyone in the company read Wooden on Leadership early on.
Wooden’s approach was about taking highly skilled individuals, in his case elite basketball players, and pairing them with strong coaching systems to create consistent, repeatable success. That maps very closely to what we do. We have people with deep subject matter expertise, professional traders, and we combine that with people who understand coaching, structure, and development. That balance between talent and coaching is a core part of how we operate.
Product obsession
On the technology and product side, Steve Jobs is an obvious influence. Zack is obsessive about the product in a way that genuinely reminds me of that mindset. He has a strong emotional attachment to what we’re building, but at the same time, he’s extremely logical and precise in how he breaks down product decisions. That combination shows up everywhere in the platform.
Elon Musk’s influence comes more from first-principles thinking. The idea of stripping problems down to their fundamentals, asking whether something is actually possible, and then building forward from there has shaped a lot of our long-term strategic thinking.
And when it comes to marketing and communication, David Ogilvy has had a huge impact. What I’ve always appreciated about his work is that he focused on progress, not just persuasion. How do you help people move forward through your marketing itself? How do they learn something just by engaging with it? That thinking is what led to the idea of the free school and a lot of our educational marketing approach.
You’ve spoken about building for long-term results rather than short-term hype. How does that philosophy influence day-to-day decision-making?
It influences everything we do, down to the smallest decisions. We’re constantly asking two questions: what is the short-term impact, and what is the long-term impact? If the long-term outcome is clearly positive, we’ll almost always choose that path, even if it hurts in the short term.
Early on, that meant giving up a lot of profitability. For the first few years, we deliberately prioritised student results over revenue. The internal decision-making is very simple. Does this improve student outcomes? If the answer is yes, that comes first. Even if it negatively impacts profitability in the short term, we still move forward, because we’re confident that strong results compound over time.
We genuinely believe that if students succeed, the business succeeds. Long term, everything else takes care of itself. That belief shows up in how we build products, how we allocate resources, and how patient we’re willing to be.
One of our core values for students is sacrifice. The idea is being willing to give up something small now for a much bigger payoff later. That principle applies to us as a company as well. Short-term sacrifices get amplified over one, two, five, or ten years. Eventually, that compounding effect becomes enormous. That’s how you avoid hype cycles and end up with something durable.
Finally, what advice would you give to founders building fintech or education platforms in highly competitive, credibility-sensitive markets?
My biggest piece of advice is to focus on a very small space and go extremely deep. Early on, spreading yourself across multiple projects or priorities is one of the fastest ways to dilute progress. You’re almost always better off putting all of your energy into one thing rather than trying to run five initiatives at the same time.
That depth comes from genuinely investing in expertise. Take something like marketing. Saying “we’re focusing on marketing” is far too broad. That’s a diffuse goal with no real direction. Instead, narrow it right down. Maybe the focus is lead generation for one specific funnel. One outcome. One number that matters. Then you put all of your energy, talent, and attention into moving that single metric.
The same applies to product and education. Decide what one outcome you’re trying to achieve and align the entire team around it. In the early stages, that level of focus is critical. When everyone in a department is working toward the same clear objective, progress accelerates dramatically.
The more energy you can concentrate on a smaller point, the further and faster you can push it. In competitive, credibility-sensitive markets, depth, clarity, and focus beat breadth every time.
