The AI boom is taking all over by the storm. Machine Learning experts earn huge pay, and AI startups are getting massive funding from investors. It is undoubtedly a great time, but is it that once-in-a-generation kind of technology that is transforming the world now? Technology is ever-changing, and it will keep changing our lives too.

Creating an AI startup is not as simple a task as it seems. There are many potholes that you could fall into if you wish to build an AI startup.

When it comes to economics, we find many complimentary items if we buy them together, like bread and eggs, cars and gasoline, etc. If the price of one complimentary thing falls, the need for the other item automatically rises. Therefore the combination of the cloud is the program built, and its corresponding AI products will also need a massive amount of computational capability. But at the same time, we have to ensure that the cost of development does not rise exponentially. We should aim to keep it as cost-effective as possible.

The development process is exciting, but it is not favorable news for people and companies who funded the AI domain. In recent times, it is an added advantage as getting the experience of a Machine Learning Engineer requires a lot of reading of papers and an ability to understand maths from the very start. But as we get access to more developing technology, this scenario may change in the future. In the end, we will get to read tutorials than just research papers to gain knowledge. If you are not aware of your advantage, you may be left behind in the race. 

Architecture and Data

In this section, we will discuss Data and how crucial it is to the expensive AI architecture. To better understand this, we suppose that two recent AI startups and their founders, Tim and Tom, raised an equal amount of funds. These two startups are highly competitive and operate in the same market. Now Tim hires top engineers and Ph.D. guys with an excellent tracking record for research in AI. On the other hand, Tom employs average engineers with relevant skills and invests some money in getting good information. So if you are an investor, which firm would you place your bet on? I would invest my money with Tom’s company. The reason is simple. The core of machine learning works by drawing information from a group of data and creating a working model. A model is efficient if its procedures (related to speed and quality) are effective. But we have to assume that the model has some initial level of adequateness, and if the data is more accurate, it will control the efficient design. 

We also see that Tim’s engineers do have competition from only Tom’s engineers. Still, they are competing with the researchers from giants like Microsoft, Facebook, Google, and many other expert groups. This is because of Tim’s AI experts’ open nature and as they focus on sharing knowledge.

If you see the best-working technology presently written in the literature and letting it train on your data is a tried and tested strategy, provided you want to resolve a problem and not a research contribution. If you do not find a solution that you could use, you might have to wait for a couple of quarters to find a viable solution. If you do not want to wait, you can start a Kaggle contest that could drive some researchers to find a solution for your specific issue.

Goof planning is essential, but if you are into AI, the data will take you ahead in the competition. It is a billion-dollar question, but it all depends if you can keep your edge.

An opinion on AI

When we try to ascertain the value of a particular thing within a company, we try to learn if it can create value with any help or support another source that makes value. To understand this, we can take an example of an online seller. If he can build a new product line, that means he could create value immediately. It did not exist before, and now, users can buy these with the help of widgets. Creating a substitutive distribution network with a difference can be another option.

An Amazon seller can increase his sales quickly. He can also reduce his costs with some effort. But if he can crack a great deal in addition with a Chinese widget manufacturer, the seller can increase his profits manifold. There are levers in a machine that can increase the needle speed more than the direct force applied to the needle. However, for a lever to operate efficiently, it needs the help of a quick value source. This number will not be small, and it could be double and even triple. But if you have no widgets to sell, then even getting hold of the distribution network will not be of any use. So will AI be of any help in this situation?

You can find many companies that attempt to make AI on their items or products. These could be APIs to identify images or any such application. The idea could be tempting for an AI expert but not viable in the long run. The main disadvantage is that you will have to compete with biggies like Amazon and Google. Secondly, it could be a nightmare to create an AI product that is specifically useful for someone. However, it is a distant dream to find a customer who is satisfied with their service. Even if you give them enough or even more, in a custom-designed development, the customer always wants more and more. It is just like attempting to insert a square peg into a round hole.

Are There Any Possible Alternatives

A possible option is if you consider AI as a helpful tool. It is a good idea to get a present business model and advance it with AI. For example, if you have a process that requires a lot of human labor, automating it with the help of AT can boost your profits to a reasonable extent. Some common examples can be of the ECG interpretation or study of satellite images. These processes use AI as their backend, and with the non-AI alternatives, you can get ahead in the competition.

Undoubtedly, AI is bringing a revolution that can transform the world, but building a startup based solely on AI could be a dangerous bet. It is not recommended to only depend on your AI capabilities as they can change anytime because of the market changes. Creating an AI model can be exciting, but you need to have more data than your competitor to succeed in the competition.

Competition becomes even more difficult if your competitor has more access to funds than you. This is a massive possibility if your AI model is a success. More important is to create a practical and efficient data collection model that is improbable to copy by your competitors. AI has all the capacity to change the industries dependent on less qualified human brains as it can replace them with automation of the process.