Mistakes AI Startups Make in Business Planning

If you want to make a lot of money in today’s market, then building a business around artificial intelligence is probably the way to go. The fact that machines now have the ability to think in a way that’s similar to humans means that intelligence is about to multiply, and probably profits as well.

The problem though is a lot of entrepreneurs in this space get too excited about the future. They take enormous risks and don’t think about the consequences of their actions. Even good business models can fail because founders adopt the wrong approaches.

Fortunately, this post is here to help. This guide explains some of the biggest mistakes that founders are making right now, and what you can do to avoid them. Make sure you read to the end.

Mistake Number One: Building The Model First And Not The Product.

Most founders fall in love with the latest technology and therefore focus on building the models. But what really counts in the marketplace is the actual product — the value that consumers are getting. Therefore, don’t be one of these AI businesses that tries to build superintelligence in the abstract. Instead, find a way to use existing systems to build intelligent technology that helps people right now.

If you can sell a product that makes a business process 10x faster, then you will earn a lot of money. Companies are constantly looking for solutions that improve their workflows, reduce burden on their staff, and make them more productive. Similarly, if you can give consumers a tool that helps them do their jobs better or anything like that, they’ll be happy to pay.

Mistake Number Two: Raising Too Much Money Too Early

The second mistake is to raise too much money too early. A lot of start-ups try to do this with pre-seed capital and sometimes can raise up to $50M. But this amount of money often leads to bloat early on which undermines the business later.

The way to get around this is to keep the business deliberately lean until it’s ready to launch your product. Even if investors are chomping at the bit to put money into your company, don’t use them until you actually need it. Make sure the core service is in place to begin with, and then accept money when you need to expand or buy a data center.

Mistake Number Three: Ignoring Distribution.

The third mistake a lot of entrepreneurs make in the artificial intelligence space is to ignore distribution. Distribution is essentially the channel you use to get your product out in front of the people who want to buy it. However, a lot of entrepreneurs focus on fancy demos and advanced technical software techniques without really thinking about how they are going to get their products in front of the people who are going to buy them.

This mistake essentially comes down to bad marketing. What you need to think about from the start of setting up an AOS is how you’re going to build the go-to-market engine. You need a system that will allow you to put your product in front of the people who are most likely to buy it and make and explain the value that it offers.

Often, it’s better to put out an MVP with a good marketing machine behind it than fully build a product that nobody knows about. Remember that right now in the AI space, there are thousands of companies looking to create products that help make consumers’ and businesses’ lives easier. If you want to stand out, you have no choice other than to start marketing.

Mistake Number Four: Bet Everything On Your Moat

If you think your AI business has a big moat around it, then think again. A lot of companies in the space think that they have significant advantages over their rivals because they’ve built these expensive frontier models. However, as recent experience shows, frontier models don’t last for very long, and it’s very easy for competitors to catch up even if they have much lower budgets. Just look at what happened with the Chinese software Deep Seek when it copied OpenAI’s LLM weights.

Therefore, look for something that’s more durable than the size of your model as your moat. For example, if you have regulatory captains in a specific area like healthcare or finance that can put you ahead of the competition. You can also beat everybody else by vertically integrating your service by connecting AI to something in the physical world, perhaps. Or you can focus on just developing a brand that people believe is better than everyone else’s. Although this is probably the shakiest approach to take.

Mistake Number Five: Only Hiring AI Experts

Mistake number five that entrepreneurs make is only hiring people with academic backgrounds in AI. There’s no doubt that these people are important to the functioning of a successful AI startup, but they shouldn’t make the core of the team. AI experts are essentially only people you need for building the hardest parts of the product. The rest of the business is fundamentally marketing and strategy.

As the entrepreneur, you’re dealing with the strategy because you are responsible for figuring out how your business is going to win. It’s your job to marshal resources. For marketing, it’s worth bringing in a marketing team or simply automating it by using an agency. If you use an agency, the quality will likely be lower, but the price will also be less steep.

Mistake Number 6: Underestimating Inference Costs

Finally, you might make the mistake of underestimating the costs of fine-tuning and training models. A lot of entrepreneurs believe that the costs are mainly borne by the companies that train the basic models like OpenAI and Anthropic. However, this isn’t the case. Fine-tuning also requires a lot of tokens.

Therefore, make sure you model the costs in advance and calculate your budget based on the AI resources you need. Check that you’re routing your fine-tuning through the most efficient model, no matter what it is.