Building Competitor Tracker - a product origin story in the age of AI :: Slobodan Stojanović — CTO and co-founder of Vacation Tracker, AWS Serverless Hero and co-author of Serverless Applications with Node.js book

21 minutes

Building Competitor Tracker - a product origin story in the age of AI

Rapid changes are sometimes scary. While working with AI is fun, it can also cause stress because it’s impossible to keep up with all the changes. Opening Twitter in the morning can lead to a mini panic attack because you missed new models that are way better than yesterday’s ones, Anthropic announced new deadlines for using their mythical models, new prompts that build products in one shot, tools, benchmarks, etc. Everything seems to be moving so fast that you forget you are in a bubble. Not the one that will burst tomorrow, but a bubble of early adopters.

While this flood of daily updates often brings mostly noise, the effects, benefits, and downsides of AI and LLMs are real. They have already affected many things. Including changing the way we build products, especially software. One of the natural consequences of these changes is that now is probably the best time to build your own product. Not the easiest time. That does not exist. Building products is playing the game on hard mode.

Let me tell you a story of a product that my co-founder, Lav, and I recently built. It’s a bit weird to write a long-form text by hand while multiple agents write and publish the code and new pages for our marketing site, but not all stories can and should be told by AI.

Did AI shift the bottleneck for building products?

As the amazing book The Goal told us many, many years ago: there’s one main limiting factor for all organizations and for building anything. The book introduces the Theory of Constraints (TOC), which explains that we can move as fast as our main limiting factor (the bottleneck) allows.

Building products has always been hard, and many things have been real bottlenecks. Starting from the idea, then your investment (time and money spent building the product), taste and organization (what to build and how), actual product building (software development, design, testing, etc.), to the whole go-to-market motion (who do you sell the product to and how do you find people that would actually pay for it). But only one of these is the bottleneck for each organization. E.g., some organizations can add developers, designers, or testers, remove items from the backlog, or change how they manage products and projects to ship features faster and speed up the production pipeline, and make their production local bottleneck less restrictive. However, even with that, go-to-market may still be a bottleneck for them. Or you might have limited time to just a few hours each week, and no budget to hire anyone else, and no way to free up more time.

The actual bottleneck varies from organization to organization and from product to product. But, if we need to pick the most common one, it’s probably go-to-market. Building products is hard, but selling them - unless you are one of the lucky marketing and sales masters, it’s extremely hard.

So, did AI shift the bottleneck?

The answer is probably yes, but depending on your bottleneck, that could be good or bad news. Let me explain!

If your actual bottleneck was a lack of time for development, I have good news for you! AI can help you write code, design the app, write marketing copy, and even test the app.

But I also have bad news.

One of my favorite laws is the now 30-year-old Tog’s Law of Commuting, which pairs with Tesler’s Law of Conservation of Complexity: “The time of a commute is fixed. Only the distance is variable.” To put it in current terms: if we can write code and ship features faster, we won’t stop with the same amount of code/features as before and go fishing. Instead, we’ll spend the same time either shipping more features or code, or building other things.

That’s not bad news - you can do more by yourself! But the bad news is that others can too. So, the go-to-market strategy becomes an even bigger bottleneck, as it’s hard to reach the right potential customers and cut through all the noise and actual competitors.

Ok, but building products is faster now, right? If that guy on Twitter one-shotted the whole application using Claude Fable 5, everyone could do that, right? Well, not really. There’s a big difference between an amazing demo app and a product. But let me tell you our backstory first.

Backstory

As mentioned in my previous article, “Employees, AI, and AI employees,” more than 3 years ago, my cofounder, Lav, and I tried to create an AI cofounder (CofounderGPT, as we called it), had a lot of fun, and failed. Well, to be honest, that’s not 100% true anymore, as we have had an AI agent called CofounderGPT working with us for the last 6 months, and it...