They told me it was impossible. A dead end.
I built a 6-figure business using a computational journalism technique that most ‘gurus’ said was a waste of time. They were wrong. Dead wrong.
For the last few years, the narrative around computational journalism has been hijacked by people who don’t actually build things. They talk about complex Python scripts, natural language processing models, and massive datasets. They make it sound like you need a PhD from MIT just to get started. It’s a lie. A convenient lie that helps them sell consulting services and expensive courses.
I’m here to pull back the curtain. I’ve had a front-row seat to the AI revolution, not just as an entrepreneur with two exits (RemoteTeam acquired by Gusto, MovieLaLa acquired by Gfycat), but as an investor in over 200 companies, including some of the biggest names in the space like Anthropic, OpenAI, and Hugging Face. I’ve seen what works and what’s just academic hot air. The truth about computational journalism is simpler, more powerful, and far more accessible than you’ve been led to believe.
The Myth of the Code-First Journalist
Let’s get one thing straight. The idea that every journalist needs to become a data scientist is a trap. It’s a surefire way to burn out, waste months of your life, and produce absolutely nothing of value.
I’ve seen it happen. I’ve advised startups that spent a year and millions in VC funding building a sophisticated “AI-powered news analysis engine.” They had a team of brilliant engineers. Their code was elegant. Their algorithms were cutting-edge.
The result? They produced a handful of articles that nobody read. Why? Because they forgot the most important part of journalism: the story.
They were so obsessed with the how that they forgot the why. They could process terabytes of data but couldn’t find a single compelling narrative inside it. They were technicians, not storytellers. The company folded in less than two years. A total loss. This isn’t a rare story; it’s the norm for companies that put tech before the narrative.
I saw another team, a media-tech startup, burn through $5 million in seed funding trying to build a platform to “democratize data stories.” Their goal was to create a push-button solution for generating articles from any dataset. The tech was impressive, a beautiful interface that could ingest a CSV and spit out a passable, if dry, news report. But they missed a critical point: data doesn't speak for itself. It needs a human interpreter, someone with context and an angle. Their automatically generated articles were soulless. They lacked a point of view. They had zero personality. The platform got some initial buzz in the tech press, but actual journalists never used it. It solved a problem that didn't exist. Within 18 months, they were out of money and the founders were looking for jobs.
This is the fundamental flaw in the mainstream view of computational journalism. It puts the technology before the narrative. It celebrates complexity over clarity. It’s an approach designed by engineers, for engineers. It is not designed for creators, for entrepreneurs, or for anyone who actually needs to build an audience and a business.
My 6-Figure “Impossible” Business
So, what’s the alternative? Let me tell you a story.
Back in 2022, I noticed a huge gap in the market. Everyone was talking about the creator economy, but the data was scattered and messy. How many creators were there? What were they earning? Which platforms were growing fastest? The answers were buried in press releases, market reports, and obscure API endpoints.
Instead of trying to build a massive, all-knowing AI to analyze it all, I did something much simpler.
- I used a simple web scraping tool—no custom code—to pull data from a dozen public sources every single day. Things like platform announcements and creator earning reports.
- I dumped all of it into an Airtable base. Not a fancy database. Airtable. The same tool people use to plan weddings.
- I spent an hour every morning just looking at the raw data. I wasn’t running complex queries. I was just… looking. For anomalies. For trends. For the one number that didn’t make sense.
One day, I found it. A small but rapidly growing platform for 3D artists was reporting monetization figures that were way out of line with its user base. The average creator was earning 10x more than on any other platform. That was the story.
I wrote a short, data-heavy blog post about it. It went viral in the 3D artist community. That single post drove thousands of subscribers to my new newsletter. Within six months, that newsletter, which was just a weekly summary of the most interesting data points from my Airtable base, was a 6-figure-a-year business built on premium subscriptions.
The business model was simple. The free newsletter went out once a week with 3-4 interesting data points and a link to the full blog post. The premium version, at $19/month, gave subscribers access to the full Airtable database, a weekly deep-dive analysis of one major trend, and a monthly Q&A session with me. The value wasn't just the data; it was the curation and the analysis. It was my perspective on what mattered. By the end of the first year, we had over 500 paying subscribers. That's over $114,000 a year from a project that started with a simple Airtable base and a web scraper.
I didn’t write a single line of Python. I didn’t train a single AI model. I used simple, off-the-shelf tools and my own curiosity. That’s the secret. The “computation” isn’t in the code; it’s in the system. It’s about creating a repeatable process to find and validate stories with data.
The Anti-Stack: My Simple Toolkit
People love to ask me about my “stack.” They expect me to list off a bunch of complex cloud services and programming languages. They’re always disappointed when I show them how simple it is.
Here’s my entire “computational journalism” toolkit:
- Data Collection: Apify or Browse AI. Why these? Because they are visual. You don't write code; you click on the data you want to extract. It's intuitive and fast. You can train a scraper in minutes, not days. They handle all the annoying stuff like proxy rotation and JavaScript rendering. It's the 80/20 of web scraping.
- Data Storage & Analysis: Airtable or Google Sheets. I chose Airtable because it's a database that thinks it's a spreadsheet. It's incredibly flexible. You can link records, create different views, and even build simple apps on top of your data. It's the perfect middle ground between a simple spreadsheet and a complex SQL database. It's all most people will ever need.
- Data Visualization: Flourish or Datawrapper. These tools are magic. You can paste in data from a spreadsheet and create stunning, interactive charts, maps, and tables in minutes. They are designed for newsrooms, so they have a high standard for visual quality and ease of use. A good chart is worth a thousand words, and these tools make it easy to create great charts.
- Writing & Publishing: Ghost or Substack. The key here is ownership. With these platforms, you own your audience. You have their email addresses. You're not at the mercy of some algorithm. Ghost is my preference because it's open-source and gives you more control, but Substack is a fantastic way to start with zero friction.
That’s it: the entire stack that powered a 6-figure media business. The total cost was less than $200 a month. The focus was never on the tools; it was on the output. It was on finding stories that no one else was telling.
The Future is Data-Driven, Not Code-Driven
So, is computational journalism dead? Yes, the version the gurus have been selling you is absolutely dead. It was a stillborn idea, an academic fantasy that was never going to work in the real world.
But the real computational journalism—the art of using simple systems to find and tell data-driven stories—is just getting started. This is the single biggest opportunity for creators and journalists in 2026 and beyond.
Forget about learning to code. Forget about complex algorithms. Focus on being a detective. Focus on your curiosity. Find a niche you care about, find the data that tells its story, and then build a system to tell that story over and over again.
Your unique advantage is not your technical skill. It's your unique perspective. It's the questions you ask. It's the connections you make that no one else sees. The tools are just there to help you find the answers faster. They are a means to an end, not the end itself.
Stop listening to the people who want to make it complicated. The future doesn’t belong to the person who can write the most complex script. It belongs to the person who can find the most compelling story.
Now go find yours.
Frequently Asked Questions
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.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
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.