How to Screw Up Your AI Startup: A Tale of Tech, Triumphs, and Traps
A story that’s a mix of comedy and tragedy, packed with valuable lessons.
Envision a bright-eyed entrepreneur (that’s me) with dreams bigger than a data center, diving eagerly into the exciting world of AI startups.
The goal? To surf the AI wave straight to Successville. However, the reality was more like a wild rollercoaster ride through a place I call Oopsie-Daisy Land. Sadly, it’s a common fact that fewer than 10% of startups make it on average.
Different ventures come with their own risks. Many of my failed startups were linked to data analytics and included elements of what we now call AI.
I want to share the tough lessons I learned while creating, testing, and trying to make my products successful in the market. So, apply these insights to your data-centric startup, potentially enhancing your odds of triumph!
1. Falling for the AI Siren Song… and Missing Out On R&D
And did I build it — a shiny, high-tech solution looking for a problem.
Picture a solution so advanced it could predict what you wanted for breakfast before your first yawn.
Cool, right? But here’s the kicker: in the rush to wow the world, I forgot to check if anyone wanted what I was cooking.
I once worked on a system to check the condition of 10kV electrical lines in the air, using data from vibrations and magnetic fields. Our goal was to understand the market, determine the issues that line operators were facing, and create the perfect signal decoder for those sensors.
We identified the problem correctly, but our research and development (R&D) moved too slowly to meet the market needs. In the end, a wireless solution from China did better than we did in our specific area. If we had adopted their technology earlier, we might have been the leading choice in the market. :)
Solving complex problems doesn’t automatically make a good business. Decide if you’re creating something new with your own R&D, which could lead to breakthroughs like very accurate predictions for supply chains. This is a deep, tech-based challenge.
Or, you could be working to fix specific problems for consumer goods companies (FMCG), making their buying process better and faster, which is more about understanding business needs. The first way is about making tech to help solve business issues.
The second way is to find out what the business problems are and then choose the best-existing tech to solve them. Trying to do both with a small team and limited resources usually leads to confusion, money problems, and loss of motivation.
2. The Tech vs. Market Tug-of-War
Turns out, launching an AI-first startup isn’t just about crafting tech marvels. It’s more like being a matchmaker between Mr. Revolutionary Tech and Ms. Market Needs.
I learned (the hard way) that you’ve got to start by cozying up to the market, understanding its quirks, and then, maybe, introducing your AI to solve real headaches. But there I was, trying to fit my “square” precious AI solution into the “round” hole of market demands. Spoiler: It doesn’t work that way.
For tech founders, it’s a tough realization that initial stages like POC or MVP often don’t need advanced tools like data science, LLMs, or the latest Python libraries. Instead, what’s crucial is simulating your future complex data service and manually replicating its operations.
Yes, it’s basic work.
Embarking on AI/DS development too early, before properly validating the problem and solution, leads to wasted time, depleted resources, and potentially, the downfall of your startup.
You might end up with a project that’s technically perfect but doesn’t truly meet client needs. Initially, you should focus on creating something basic but operational — like a mobile app or web interface — to draw in users.
Early on, tasks such as matching, forecasting, and recommending should be done manually before transitioning to more sophisticated methods like neural networks and OpenAI APIs. This approach isn’t about scalability but about proving the value of your concept.
Once proven, you can confidently employ advanced technology to enhance your solution.
3. Data Drama
Then came the data drama. Imagine having a supercar but no fuel. That was me with my AI model — all dressed up with nowhere to go because, oops, I skimped on the data.
Quality data is the hero of AI; it’s what makes your AI model go from “meh” to “wow.” But did I realize that in time? Of course not. Lesson learned: Data isn’t just another item on the checklist; it’s the secret sauce.
When starting to build, it’s essential to have a clear understanding of the problem and access to extensive, relevant data. High-quality, up-to-date data is key to developing accurate models.
A note to non-tech founders: don’t expect miracles from your CTO or co-founder if you only provide them with a small dataset like a 120-row Excel file from a client. In data science and AI, vast amounts of data are often needed, sometimes even millions of data points. You might consider setting up a pilot project to get quality data, particularly if you’re creating a B2B SaaS product. Getting the right data is just as important as involving key decision-makers in the process.
For instance, I was part of starting an app designed to cut data center power consumption by up to 20% by predicting server workload. However, the challenge was finding real workload data.
We quickly created a shell script to semi-randomly generate CPU/RAM usage patterns of different lengths using the “stress” tool and scheduled it with cron. Surprisingly, we started accurately predicting the next 15 minutes of workload on our test server within weeks. I even bought an HP 1U server for experiments at home despite the loud noise.
Six months later, we landed our first potential client — a major data center operator — and installed our monitoring tool on various server types. But then came the letdown. The real-world workload data varied in seconds, not minutes or hours, and had much smaller changes than expected. Our prediction system couldn’t adjust, learn, or predict accurately.
The lesson learned was clear: “Next time, we start with the data.”
4. Finding the Right Balance
Kicking off our startup journey is super exciting, but there’s a trick to keeping it smooth.
Right from the get-go, we must be sharp about our choices. It’s like picking the best tools that don’t cost an arm and a leg but still grow along with our big dreams. And when it’s time to let the world in on our awesome ideas, let’s be a bit sneaky about it.
Share enough to spark interest but keep the magic secret to protect it.
When you begin your journey with an AI startup, it might seem tempting to hire costly experts and build very complicated technology right away. But think again before opening your wallet.
In the early days, it’s smarter and cheaper to use simple, well-known methods. This means doing things in a straightforward way that doesn’t waste money and makes sure you’re solving problems that need solving. Jumping into fancy technology like AI that guesses what you want before you might sound cool, but it’s important to ask if you’re ready for it and if people need it.
Sharing your big idea can be risky, especially with bigger companies that can take your idea and do it themselves. Also, depending on outside technology, too much can be a problem if it turns out to be too expensive or doesn’t work how you need it to.
You have to be careful about who you tell your innovative ideas to and make sure you’re making smart choices about the technology you use. This way, you keep control over your innovations and make sure they stay yours.
The Takeaway: A Dash of Humor, A Ton of Wisdom
So, fellow dreamers and doers, that’s the tale of how I surfed, stumbled, and learned in the AI startup saga.
It’s a journey of missteps, facepalms, and, eventually, wisdom. Remember, the path to startup stardom isn’t just about tech glitz; it’s about romancing the market, waltzing with data, and riding the trend wave with a keen eye on where it’s headed. So gear up, stay nimble, and maybe, just maybe, you’ll crack the code to startup success in the world of AI.
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