They’re everywhere, aren’t they? The self-proclaimed AI gurus, flooding your social media feeds with promises of automated riches. They flash screenshots of seven-figure trading accounts, all supposedly managed by a magical black box they built in a weekend. They’ll sell you a course, a signal, a dream of push-button wealth. It’s a fantasy. And as someone who has spent his life in the trenches of Silicon Valley, building and investing in the very technology they claim to have mastered, I’m here to tell you: it’s a lie.
I’m Sahin Boydas. I’m not a guru. I’m an engineer. I’ve started four companies, and I’ve been fortunate enough to have two successful exits: RemoteTeam, which was acquired by Gusto, and MovieLaLa, which was acquired by Gfycat. I’ve also been an active angel investor, with over 200 investments in companies like Anthropic, OpenAI, Scale AI, and Hugging Face. I’ve seen the hype cycles come and go. I’ve seen what works and what doesn’t. And I’m here to give you the ground truth about AI in finance.
The Guru Fantasy vs. The Gritty Reality
The gurus want you to believe that AI is a magic wand you can wave to print money. The reality is that building and implementing AI in finance is a messy, grueling, and often thankless job. It’s less about writing a few lines of Python and more about wrestling with mountains of messy, unstructured data. It’s less about finding the perfect algorithm and more about the painstaking process of cleaning, labeling, and preparing your data so that an algorithm can even begin to make sense of it.
I remember when we were building out the payroll system for RemoteTeam. We were dealing with a spaghetti-mess of international regulations, different currencies, and a dozen different payment providers. We thought we could just throw some AI at the problem and it would all sort itself out. We were wrong. It took us months of painstaking work, with a team of engineers and legal experts, to build a system that could handle the complexity. And even then, it wasn't perfect. We were constantly iterating, constantly fixing bugs, constantly dealing with edge cases that our models had never seen before.
That’s the reality of AI in finance. It’s not a black box that you can just turn on and forget about. It’s a complex system that requires constant care and feeding. It’s a human-in-the-loop system, where the AI is a tool to augment human intelligence, not replace it.
What AI is Really Good At in Finance (and What It's Not)
So if AI isn’t a magic money-printing machine, what is it good for? A lot, it turns out. Just not the things the gurus are selling.
Where AI Shines:
- Fraud Detection: This is one of the oldest and most successful applications of AI in finance. Banks and credit card companies have been using machine learning for decades to detect fraudulent transactions. And the technology is only getting better. With the rise of deep learning, we can now build models that can detect even the most subtle patterns of fraudulent behavior.
- Risk Management: AI is also incredibly powerful for assessing and managing risk. Hedge funds and investment banks are using AI to build sophisticated models that can predict market movements, identify potential black swan events, and optimize their portfolios for risk and return. I’ve invested in several companies that are doing amazing work in this space.
- Process Automation: This is where I see the most immediate potential for AI to transform the finance industry. There are still so many manual, repetitive tasks in finance that are ripe for automation. Things like data entry, reconciliation, and compliance reporting. By automating these tasks, we can free up human workers to focus on higher-value activities.
Where AI Struggles:
- Predicting the Market: This is the holy grail of AI in finance, and it’s also the most elusive. The financial markets are a complex, chaotic system with an almost infinite number of variables. And while AI can help us find patterns and correlations, it’s not a crystal ball. Anyone who tells you they have a model that can consistently predict the market is either lying or delusional.
- Replacing Human Traders: For all the talk about robo-advisors and automated trading, the best traders are still human. And I don’t see that changing anytime soon. The best traders have a deep understanding of the markets, a sixth sense for risk, and the ability to make decisions under pressure. These are all things that are very difficult to replicate with a machine.
The
Real Secret to Winning with AI in Finance
So if you can’t trust the gurus, and you can’t just buy a magic black box, what’s the secret to winning with AI in finance? It’s boring, but it’s true: it’s all about the data. The best AI models are built on the best data. And the best data is clean, well-structured, and relevant to the problem you’re trying to solve.
This is something I learned the hard way at MovieLaLa. We were trying to build a recommendation engine for movies, and we had a ton of data on what movies people were watching. But the data was a mess. It was full of duplicates, missing values, and inconsistent formatting. We spent months cleaning and preparing the data before we could even start building our models. But it was worth it. Once we had a clean dataset, we were able to build a recommendation engine that was far more accurate than anything our competitors had.
It’s the same in finance. The firms that are winning with AI are the ones that have the best data. They’re the ones that have been collecting and curating data for years, and they have the infrastructure to process and analyze it at scale. They’re not just throwing a bunch of data at a model and hoping for the best. They’re being strategic about what data they collect, how they store it, and how they use it to train their models.
My Advice to You: Be a Builder, Not a Gambler
So what does this all mean for you? If you’re a founder, an investor, or just someone who’s trying to make sense of the AI revolution in finance, here’s my advice: don’t be a gambler. Don’t chase the latest get-rich-quick scheme. Don’t buy into the hype. Instead, be a builder. Focus on solving real problems for real people. Focus on building a sustainable business with a strong foundation.
And if you’re going to use AI, use it as a tool to help you build that business. Use it to automate repetitive tasks, to gain insights from your data, and to make better decisions. But don’t expect it to be a magic bullet. Don’t expect it to do the hard work for you.
The truth is, there are no shortcuts in finance. There are no get-rich-quick schemes that actually work. There’s just hard work, smart decisions, and a little bit of luck. And that’s something the gurus will never tell you.
Frequently Asked Questions
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.