How to invest in AI — Part 1

Image generated with DALL-E-2 using the prompt “hand putting a coin into a robot.”

The percentage of investments that are AI related has gone up. It almost seems as if every contemporary software startup revolves around the phenomenon of Artificial Intelligence (AI).

In my job at building and leading one of the biggest AI Startup Incubators in Europe, working closely with various founders, and teaching a course on AI for innovation and entrepreneurship at the Technical University of Munich, I screen a lot of pitch decks on AI and am privy to a great deal of AI startups as they come and go. Frequently I am asked by investors and VCs to examine AI startups and help them to make their investment decisions. In this series of posts, I wish to share my investment hypotheses and explain what I look for in evaluating and investing into AI startups.

How to Invest in AI — Part 2
How to Invest in AI — Part 3 (Due Diligence)

The challenge of investing in AI startups.

“I don’t think I’ve seen a PowerPoint without the word AI in the last two years. It is almost like software equals AI.” Brian Ascher, Partner at Venrock.

Investments in AI have been steadily going up over the years.

Investing in AI startups can be challenging. Many startups claim to use artificial intelligence, but in reality, they may not be using AI in a meaningful way, or may not have the technical expertise to effectively develop and deploy AI solutions. This can make it difficult for investors to accurately evaluate the potential of such companies. This situation has created a bubble in the AI startup market, with many companies claiming to use AI in order to attract investment. So-called “snake oil AI startups” are companies that claim to use AI in their products or services. In reality, they may not be using AI at all or may be using it in a superficial or misleading way. These types of startups are often more interested in generating hype and attracting investment than in delivering real value through the use of AI. One study even claims that “forty percent of ‘AI startups’ in Europe don’t actually use AI.“ This can make it difficult for investors to distinguish between legitimate AI startups with real potential and those that are simply riding the hype.

Additionally, in my experience, I have noted that many investors lack a deep understanding of AI and how it works. This lack of sufficient knowledge and experience can make it challenging to accurately assess the viability of an AI startup. The problem is as follows: when investors see large sums of money flowing into AI, they may invest without fully understanding the technology or completing an assessment of whether the investment will actually provide investment returns over time. In order to avoid the bubble and make good investments in AI, VCs and investors should be asking themselves how they can de-risk their investment with an understanding of technology and its potential for future returns. The necessary due diligence must also be comprehensively undertaken.

Here is my guide for the savvy AI investor:

Step 1 — Understanding the difference between AI and ML, and the value startups can create using AI

Artificial intelligence (AI) and machine learning (ML) are often used interchangeably, but they are not the same thing. AI refers to the ability of a machine to perform tasks that would normally require human intelligence, such as recognizing patterns, making decisions, or solving problems. Machine learning, on the other hand, is a subfield of AI that focuses on the development of algorithms and statistical models which allow machines to improve their performance on a particular task over time by learning from data, rather than being explicitly programmed.

One reason these terms are often mixed up is that machine learning is a key component of many AI systems and is used to enable such system to learn and adapt. However, not all AI systems use machine learning, and not all machine learning systems are considered AI.

Mixing up these terms can cause problems because it can lead to misunderstandings or expectations that are not met. For example, an investor may expect an AI startup to have developed advanced, self-learning algorithms when in reality, the startup is simply using basic rule-based systems. Similarly, a customer may expect an AI-powered product to be able to adapt and improve over time, but if the product is not using machine learning, it will not be able to do so.

What is the crucial difference investors need to understand?

In order to avoid misunderstandings and set realistic expectations, it is important to firstly understand the difference between traditional programming and machine learning and secondly, to accurately communicate which technologies a company is using.

There are two different ways of how programming works. Rule-based (traditional) programming and machine learning programming are two different approaches to building software systems.

Rule-based programming involves creating a set of explicit rules or conditions that a software system follows to perform a task. For example, a rule-based system might be programmed to send an email to a customer if certain conditions are met, such as if their account balance falls below a certain threshold. Rule-based systems are generally good at following precise instructions and are easy to debug, but they can be inflexible and may not be able to adapt to changing conditions or new situations.

Machine learning programming involves building algorithms that allow a software system to learn from data and improve its performance on a particular task over time. Machine learning algorithms are trained on a large dataset and use statistical models to find patterns and make predictions or decisions. They do not require explicit rules or conditions to be programmed in advance, but instead learn from the data they are given. Machine learning systems can be more flexible and adaptable than rule-based systems, but they can also be more complex and require more data and computational resources to train and operate.

Both rule-based and machine learning programming approaches have their strengths and limitations, and the appropriate approach will depend on the specific task and the goals of the system.

Understand how AI can deliver value

Understanding the value that AI can create is important because it can one help assess the potential risks and opportunities associated with an AI startup, thereby ensuring the potential investor makes more informed investment decisions. By understanding the ways in which AI can create value, I can evaluate the growth potential and long-term viability of the startup I am looking at.

AI can create value in a number of ways, depending on the specific application and use case. Some of the ways in which AI can create value include:

1. Improving efficiency and productivity: AI can automate certain tasks or processes, enabling organiyations to work more efficiently and effectively.
2. Enhancing decision-making: AI can analyse large amounts of data and provide insights or recommendations that can help organisations make better decisions.
3. Creating new revenue streams: AI can enable organisationsto offer new products or services, or to target new markets, which can help to drive revenue growth.
4. Reducing costs: AI can help organisationsto streamline their operations and reduce costs, which can improve profitability.

Read on: How to Invest in AI — Part 2

Disclaimer: I have written this article using my own experience and know-how regarding I approach the evaluation and due diligence of an AI startup. To accelerate the writing I used the help of ChatGPT. However, I manually added examples, interviews, images and references; I tweaked the inputs and outputs and rewrote and added paragraphs. Using ChatGPT allowed me to write this series of posts in a couple of days — instead of weeks.