How to invest in AI — Part 2

Image generated with DALL-E-2 using the prompt “digital art of investors investing into robots.”

This is part 2 of my series of blog posts on how to invest in 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 1
How to Invest in AI — Part 3 (Due Diligence)

Step 2 — Figure out if the startup is using AI as a core technology or as an enabling technology.

Knowing whether a startup is using AI as a core or enabling technology is important as this can impact the potential value of the company and the level of risk involved. If AI is a core technology, the startup may be more reliant on the success of its AI products or services. However, if AI is an enabling technology, the startup may have more diversified sources of revenue. It is important to carefully evaluate the role of AI in the startup’s business model and consider the potential risks and rewards before making an investment decision.

To determine if a startup is using AI as a core technology or an enabling technology, you should consider the following:

1. Product or service: Is the startup’s product or service primarily focused on AI, or is AI being used to support or enhance an existing product or service? If the AI technology is central to the product or service, it is likely a core technology. If it is being used to support or augment an existing product or service, it is likely an enabling technology.
2. Market positioning: How is the startup positioning itself in the market? Is it marketing itself as an AI company, or is it highlighting the use of AI in its products or services?
3. Team expertise: Does the startup have a team with strong expertise in AI, or is the team primarily focused on a different area, such as product development or marketing, and using AI as an enabling technology?
4. Use of data: Does the startup have a large amount of high-quality data, which is essential for training and improving AI models? If so, this indicates that AI is a core technology for the startup.

Step 3 — Try to classify the AI startup and understand how this affects the value proposition.

The value proposition of an AI startup will depend on the specific products or services it offers and the market it is targeting. For example, an AI startup that offers a unique, differentiated AI software product may have a strong value proposition, while an AI startup offering a commodity AI service may have a weaker value proposition. It’s important to carefully evaluate the value proposition of an AI startup when considering an investment.

There are many different types of AI startups. The value proposition of each will depend on the specific products or services they offer and the market they are targeting. Some common types of AI startups include:

1. AI enabling software: These startups develop and sell AI software products, such as machine learning libraries, natural language processing tools, data labelling tools, and no-code solutions. One example is the company Hugging Face which develops tools for building applications using machine learning. It is most notable for its library built for natural language processing applications.
2. AI-powered products or services: These startups offer products or services that use AI to solve specific problems or meet customer needs. Examples include AI-powered customer service chatbots, predictive maintenance tools, and personalised recommendation engines. Examples include self-driving cars, autonomous drones, or the startup Gong which is dedicated to solving the typical problems in the sales and customer area with the help of AI. To help companies improve their customer success and sales performance, Gong tracks every customer interaction, such as sales calls and emails, and evaluates important factors such as conversation topics, and the duration or frequency of customer conversations, using natural language processing and machine learning.
3. AI-enabled products or services: These startups use AI to enhance or support existing products or services, but AI is not the primary focus of the business. Ant Financial uses AI to deliver financial services at almost zero (human) operational costs. For instance, they have automated their loan application process to the point where they claim to have no human involvement when it comes to granting loans. Another example is ByteDance’s TikTok, which provides users around the world with short video clips recommended by an AI based on their preferences, viewing habits and location.
4. AI hardware: These startups develop hardware specifically designed for AI applications, such as specialised chips or sensors. One example is the German startup Konux, which combines machine learning and Industrial IoT to deliver Software-as-a-Service (SaaS) solutions for operation, monitoring, and maintenance process automation. Their sensors are used on railway switches to increase the availability of the track network and thus improve the punctuality of trains.
5. AI consulting: These startups offer consulting services to help businesses integrate AI into their operations or develop custom AI solutions. Usually AI consulting companies such as appliedAI or Merantix would not be the classical VC case as they are (human) resource intensive and therefore do not scale well.

It is important to note that it is not always possible to make a clear distinction between the types of startups. Konux’s IOT devices, for example, comprise both AI hardware as well as an AI-powered product.

Step 4 — Understand the most common challenges of AI startups.

Understanding the most common challenges of AI startups can help if you want to make a quick initial assessment. Oliver Schoppe, early stage investor at UVC Partners, talks in an article about the most common pitfalls of AI startups.

Product Market Fit

Ensuring product market fit is a critical challenge for AI startups, as this can impact the startup’s ability to attract customers, generate revenue, and ultimately achieve profitability. It’s important for AI startups to carefully assess their target market and ensure that their products or services meet the needs and desires of their customers. According to Schoppe: “It’s so tempting to get passionate about beautifully crafted tech. Especially since AI often yields impressive show cases early on. I am the first one to get excited about it. But here’s an ugly truth: no one pays for this. People pay you to solve their problems, that’s all it comes down to. When aiming to build a sustainable business, never start (only) with tech. Start with obsessively investigating your customers and their problems. That is your one and only north star.”

There are several reasons why finding the right product market fit is not easy:

1. Complex technology: AI can be a complex and rapidly evolving field, which can make it challenging for startups to develop products or services that are both innovative and practical.
2. Limited data: Many AI solutions require large amounts of high-quality data to train and improve their performance, which can be a challenge for startups that don’t have access to enough data or the resources to collect it.
3. Limited expertise: AI startups may struggle to find the right talent or expertise to develop and deploy their products or services, particularly if they are operating in a niche market.
4. Regulatory challenges: Some AI solutions, particularly those in industries such as healthcare or finance, may be subject to regulatory requirements that can be difficult for startups to navigate.
5. Competition: The AI startup market is highly competitive, and startups may struggle to stand out and differentiate themselves from their competitors.

Scalability

Scalability is one of the most common challenges faced by AI startups. Scalability refers to the ability of a business to grow and expand its operations without encountering diminishing returns or increasing costs.

“Half of the AI teams I talk to have built great tech to solve a problem for their customers and earned good money while doing so” says Schoppe, “but this early success can be misleading. Developing a useful AI solution for one customer is easy, developing an AI solution that works for 10 or 100 customers without retraining is exponentially more difficult.”

AI startups may face challenges in scaling their businesses for a few reasons:

1. Resource constraints: Many AI solutions require significant computational resources and data to train and operate, which can be a challenge for startups that don’t have access to the necessary infrastructure or funding to scale.
2. Limited data: As mentioned above, many AI solutions require large amounts of high-quality data to improve their performance, which can be a challenge for startups that don’t have enough data or the resources to collect it.
3. Talent: AI startups may struggle to attract and retain the necessary talent to scale their operations, particularly if they are operating in a niche market or are located in an area with limited tech talent.
4. Market demand: Even if a startup is able to overcome the technical and resource challenges of scaling its AI solutions, it may still struggle to scale if there is not enough demand for its products or services in the market.
5. Regulatory challenges: Some AI solutions, particularly those in industries such as healthcare or finance, may be subject to regulatory requirements that can be difficult for startups to navigate as they scale.

One specific challenge that AI startups may encounter when trying to scale is heterogeneous data, which refers to data that is diverse, unstructured, or derived from multiple sources. Heterogeneous data can make it difficult to build and train machine learning models that are accurate and reliable, as it may be more challenging to identify patterns and correlations in the data. This can cause many AI startups to go from pilot to pilot, testing their solutions on small, specific datasets, without being able to create a product that scales well and can handle a wide range of data.

I encountered similar problems when working for an AI startup in the medical sector. We tried to build models that would help radiologists analyse CT scans. However, each scanner generates slightly different images, which requires us to fine tune the models for each new scanner.

Creating a long-lasting unique selling point

It can be challenging for many AI startups to create a long-lasting unique selling point (USP) because the field of AI is rapidly evolving and highly competitive.

“Almost no field of technology develops as fast as AI. The state-of-the-art today is the commodity of tomorrow. And I mean it. If your startup’s sole USP today is your AI solution, you run a very high risk of losing any defensibility within two years. Software eats the world, but tomorrow’s AI will eat today’s AI.” - Schoppe.

A USP is a unique feature or benefit that sets a product or service apart from its competitors and makes it attractive to customers. In the case of AI startups, some potential USPs could include innovative AI technology, access to large amounts of high-quality data, or expertise in a specific domain or application.

However, creating a long-lasting USP can be challenging for AI startups for several reasons:

1. Technological advancements: AI is a rapidly evolving field; what may be considered innovative or unique today may quickly become commonplace or obsolete in the future. This can make it difficult for AI startups to maintain a unique selling point over time.
2. Competition: The AI startup market is highly competitive, and it can be difficult for startups to differentiate themselves from their competitors. Many startups may be working on similar problems or technologies, and it can be challenging to create a truly unique solution.
3. Customer preferences: Customer preferences and needs can change over time; what was once considered a USP may no longer be attractive to customers. This can make it difficult for AI startups to maintain a long-lasting USP.
4. Regulatory challenges: Some AI solutions, particularly those in industries such as healthcare or finance, may be subject to regulatory requirements, such as proposed by the AI Act, that can impact their ability to offer unique features or benefits.

Creating a long-lasting USP is a critical challenge for AI startups, as it can impact their ability to attract and retain customers, generate revenue, and achieve profitability. It is important for AI startups to carefully assess their target market and identify unique features or benefits that will meet the needs and desires of their customers over the long term.

Read on: How to Invest in AI — Part 3 (Due Diligence)

Disclaimer: I have written this article using my own experience and know-how regarding how 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, rewrote, and added paragraphs. Using ChatGPT allowed me to write this series of posts in a couple of days — instead of weeks.