Chapter Six: Assessing AI Startups: The New Due Diligence

Decoding the Potential: A Deep Dive into the Due Diligence of AI Startups

1. Introduction

Navigating the dynamic and rapidly changing landscape of AI startups is a thrilling venture, overflowing with promising opportunities. However, it's a complex journey filled with high stakes and inherent challenges. These realities bring to the fore the critical necessity for thorough due diligence when assessing potential investments in AI startups.

This chapter seeks to unpack the intricate process of due diligence within the context of AI startups. We will immerse ourselves in critical aspects such as assessing the team's AI talent, gauging the genuine value that the product brings to the table, undertaking a detailed market analysis for a robust product-market fit, and evaluating considerations around intellectual property rights.

The primary objective here is to offer a comprehensive guide that allows investors to methodically assess the potential of an AI startup. The information we present aims to empower investors to make informed decisions underpinned by meticulous analysis. As the AI startup ecosystem continues to evolve at a breakneck pace, maintaining agility and staying well-informed become critical to identifying opportunities that could radically transform industries and yield significant returns on investment.

So, let's embark on this journey to understand the intricacies of performing due diligence when assessing AI startups.

2. Understanding the Team: The Importance of AI Talent

The importance of human capital in determining the success of startups cannot be overstated, especially when it comes to the fast-paced and technically complex realm of AI. The calibre of a startup's team has always been a significant factor in assessing its potential. This significance becomes even more critical when it involves AI startups, where the incorporation of AI into the core value proposition adds a layer of complexity to the mix.

In the world of AI startups, the collective knowledge, skills, and experience of the team often act as a key differentiator. A strong AI-focused team can more effectively navigate the technical hurdles associated with developing and implementing AI solutions. They can also more accurately predict and respond to changes in the AI landscape, giving their startup a competitive edge.

As the prominence of AI continues to increase, so does the demand for experts in the field. An AI startup with a team that includes AI specialists—such as data scientists, machine learning engineers, and AI ethicists—demonstrates a serious commitment to understanding and leveraging AI technologies effectively. It also indicates that the startup is likely to be more capable of addressing the specific challenges associated with AI, such as data management, algorithm development, and compliance with evolving AI regulations.

Therefore, when performing due diligence on an AI startup, a careful evaluation of the team's AI talent is essential. Investors need to assess the team's technical expertise, their understanding of the AI market, and their ability to execute the startup's AI-driven vision. By doing so, they can gain valuable insights into the startup's potential for success in the competitive AI landscape.

Do Generative AI Models Make AI Talent Irrelevant?

In the face of the rise of generative AI models, a question that often arises is whether these technologies make the human talent in AI irrelevant. While generative models such as OpenAI's GPT have indeed demonstrated remarkable abilities, from generating human-like text to creating complex software code, the need for AI talent within startups remains as crucial as ever.

Generative AI models are powerful tools, but they still operate within the frameworks, objectives, and limitations set by humans. The design, development, deployment, and maintenance of these models require a deep understanding of AI principles, algorithms, and techniques. Even with the most advanced models, important decisions around data preparation, model selection, training, validation, and interpretation of results need human oversight. Additionally, potential ethical, legal, and societal implications of AI systems necessitate the presence of informed humans who can navigate these complexities.

Moreover, AI talent in a startup extends beyond technical expertise. It includes the ability to envision innovative uses for AI, align AI strategy with business goals, manage AI projects, and communicate effectively about AI with various stakeholders. Even as AI continues to advance, these are skills that generative models cannot replicate.

Generative AI models are transforming what is possible, but they are tools that need to be directed and fine-tuned by skilled humans. Therefore, when assessing an AI startup, the presence of AI talent remains a vital consideration. The sophistication of a startup's AI models should not overshadow the importance of the team behind them.

Case Studies Highlighting Successful Startups Backed by Strong AI Talent

PathAI: is a leading example of how a robust AI team can drive innovation. The startup, which focuses on improving diagnostic accuracy in pathology with AI, was co-founded by Dr. Andy Beck, who holds a PhD in Applied Mathematics and has an extensive background in biomedical informatics, computational biology, and pathology. His combination of domain expertise and AI knowledge enabled the company to develop an AI platform that helps pathologists diagnose disease more accurately and efficiently. The AI team at PathAI continues to evolve their models and tackle complex problems in medical diagnostics, showcasing the crucial role of AI talent.

OpenAI: Perhaps no company exemplifies the importance of AI talent as much as OpenAI. Founded by a team of AI luminaries including Elon Musk and Sam Altman, OpenAI's mission is to ensure that artificial general intelligence (AGI) benefits all of humanity. The organization's strength lies in its team of top-tier AI researchers and engineers who work on cutting-edge AI models like GPT-3 and DALL-E. OpenAI's significant contributions to AI research and development illustrate the transformative power of a strong AI team.

DataRobot: offers an enterprise AI platform that automates and accelerates the process of building, deploying, and maintaining AI models. It was founded by Jeremy Achin, a data scientist and Tom de Godoy, a software engineer, both of whom had prior experience in data science and machine learning. Their AI expertise enabled them to build a platform that makes AI accessible to businesses, demonstrating how strong AI talent can lead to innovative solutions that democratize AI.

These case studies highlight how AI talent plays a pivotal role in a startup's ability to innovate, execute, and deliver value. The human expertise in designing, training, implementing, and iterating AI models is a fundamental factor that cannot be disregarded during the due diligence process of assessing AI startups. Even as AI technology continues to evolve, the importance of human talent in driving AI innovation remains constant.

Assessing AI Expertise: A Daunting Challenge

Given the pivotal role of AI talent in a startup's potential, understanding how to assess it effectively becomes paramount. However, this task is far from straightforward due to a few key challenges:

1. Technical Complexity: AI is a technically complex field that involves deep knowledge of various algorithms, machine learning techniques, data engineering, and more. Unless an investor has a strong background in AI or related fields, it can be difficult to evaluate the technical competency of an AI team accurately.

2. Rapid Evolution: AI is a rapidly evolving field, with new algorithms, tools, and techniques emerging regularly. A team that stays abreast of these changes and demonstrates the ability to adapt quickly is valuable, but gauging this adaptability can be challenging for those not deeply involved in the field.

3. Balancing Domain Knowledge and AI Expertise: A successful AI startup usually needs a balance of deep domain knowledge and AI expertise. For instance, an AI startup in the healthcare space would ideally have a team with both medical and AI expertise. Assessing this balance and understanding the interplay between domain knowledge and technical expertise can be tricky.

4. Evaluating the Practical Application: Beyond theoretical knowledge, it's important for an AI team to demonstrate practical application skills. They should have experience in deploying AI solutions that work in real-world conditions, dealing with issues like skewed or insufficient data, and ensuring the robustness and reliability of AI models. However, without technical AI knowledge, it can be hard to assess a team's skills in these areas.

5. The 'AI Hype': Many startups claim to use AI, but the reality might be that they use simple rule-based algorithms, or that their AI does not add significant value to their product (I’ve seen this first hand in my travels). Cutting through the hype to determine whether a startup has meaningful AI integration can be one of the most challenging aspects of due diligence.

Overcoming these challenges requires a combination of education, consultation with AI experts, careful questioning, and discernment. While mastering AI may not be feasible for every investor, having a basic understanding and knowing what questions to ask can go a long way.

The following sections will delve into strategies for evaluating the product and market fit, providing more tools for potential investors to conduct comprehensive due diligence in the AI startup landscape.

3. Evaluating the Product: Does the AI Add Real Value?

 Criteria for evaluating whether an AI product is genuinely innovative or just hype.

Assessing the AI product's real value is a crucial part of the due diligence process. This involves a deep dive into whether the AI application genuinely offers innovation or if it's just another buzzword-fueled hype.

So, what does it mean for an AI product to add real value? Essentially, it's not enough for a product to just incorporate AI; the AI should be integral to the product's value proposition. It should provide significant enhancement in features, performance, or customer experience that wouldn't be possible with traditional methods.

Here are some criteria to help discern the real value of an AI product:

1. Innovation: Consider whether the AI is being used to solve a problem in a new and innovative way, or if it provides a solution that outperforms existing non-AI solutions.

2. Utility: Does the AI provide a substantial utility to users? Does it enhance user experience, streamline operations, improve results, or provide any other tangible benefits?

3. Competitive Advantage: Can the startup's competitors easily replicate the AI features, or does it provide a substantial competitive edge?

4. Scalability: AI is particularly well-suited for tasks that need to scale, as it can automate and speed up processes that would be time-consuming for humans. Does the AI product provide a solution that can scale efficiently?

5. Data Strategy: AI thrives on data. Does the startup have a strategy to acquire, manage, and utilize data effectively? Moreover, does the company use the data to continuously improve the AI model's performance over time?

6. Robustness and Reliability: How well does the AI system perform under a variety of conditions? Does it handle exceptions well? If the product is customer-facing, is the AI reliable and robust enough to ensure a good user experience?

Remember, a product touting AI capabilities isn't necessarily innovative or valuable. The AI should be integral to the product, not just an ancillary feature. It should solve a genuine problem or need, offer significant benefits to the users, and ideally provide a competitive edge to the startup. Assessing these factors will help you determine if the AI product adds real value or is merely AI hype.

Case studies illustrating AI products that have brought significant value to their industries.

Let's delve into a few examples of AI startups that have successfully harnessed AI to create products that deliver genuine value to their industries.

DeepMind's AlphaFold in healthcare is an excellent example of an AI product that has brought about significant change. DeepMind, an AI research lab acquired by Google, developed AlphaFold, an AI system that predicts the 3D structures of proteins with remarkable accuracy. This achievement was hailed as a solution to a problem that has confounded scientists for 50 years, and it has the potential to revolutionize drug discovery and disease understanding.

Darktrace is a cybersecurity startup that has effectively utilized AI for threat detection and response. Its AI-driven 'Enterprise Immune System' learns a 'pattern of life' for every user, device, and network within an organization and can identify subtle deviations indicative of a cyber threat. This preemptive threat detection approach has resulted in immense value to organizations in maintaining their cybersecurity defenses.

OpenAI's GPT-3 model represents an important advancement in natural language processing (NLP). With 175 billion machine learning parameters, GPT-3 excels at understanding and generating human-like text, capable of writing essays, answering questions, and even generating poetry that's nearly indistinguishable from those written by humans. This NLP technology has far-reaching implications for various industries, from customer service (automated chatbots) to education (personalized learning assistants).

These case studies underscore the transformative potential of genuinely innovative AI products. In each case, the AI isn't just a bolt-on feature – it's the core of the product that drives significant value for their users and respective industries. These examples also demonstrate the diversity of fields where AI can bring game-changing innovations, from healthcare to cybersecurity, to language processing.

Discussion on the Challenges Investors Face When Assessing the Value of an AI Product

Discussion on the Challenges Investors Face When Assessing the Value of an AI Product

Evaluating the true value of an AI product can be a challenging task for investors, primarily because it requires a nuanced understanding of both the technical aspects of AI and its applicability in a given market context. Let's discuss some of the key challenges investors face when assessing the value of an AI product.

1. Technical Complexity: AI technology can be complex and arcane to those without a background in the field. While an investor need not be an AI scientist, understanding the basic principles and being able to differentiate between genuinely transformative AI and mere hype is vital.

2. Overhyped AI Claims: AI is a buzzword that many startups use to attract funding, even if their product only uses AI in a superficial way. This makes it challenging for investors to differentiate between startups that genuinely leverage AI for significant value and those that use it as a mere marketing strategy.

3. Lack of Standard Evaluation Metrics: AI technologies, such as machine learning models, don't have standard evaluation metrics that apply across all use cases. Performance can vary significantly depending on the specific application, making it difficult for investors to compare different AI products objectively.

4. Market Understanding: Even if a product is genuinely innovative from a technical perspective, it might not necessarily bring value if there's no market need for it or if it doesn't align with user behaviors and preferences. Investors must have a keen understanding of the market to accurately assess the potential value of an AI product.

5. Regulatory and Ethical Implications: AI products often come with unique regulatory and ethical considerations. Investors must navigate this landscape to understand how these factors might impact the product's value. For instance, an AI application in healthcare may have to deal with regulations around patient data privacy, while an AI hiring tool must be wary of inadvertent algorithmic bias.

These challenges highlight the need for investors to approach AI startup assessment with a discerning eye, armed with both technical and market knowledge. It underscores the importance of the due diligence process we're exploring in this chapter.

4. Market Analysis: Is There a Strong Product-Market Fit?

Explanation of the concept of product-market fit in the context of AI startups.

In the world of startups, achieving product-market fit (PMF) is considered a significant milestone. But what does it mean in the context of AI startups, and why is it important for investors?

Product-market fit is when a company’s product satisfies a strong market demand. It's the moment when a startup finally finds its ideal customers, and its product resonates deeply with them. It's a clear sign that the startup is serving a real market need with a viable solution.

For AI startups, achieving PMF can be even more challenging than for traditional startups due to several unique factors:

1. Technical Complexity: AI technology often brings added complexity to a product. This complexity can sometimes make it harder for potential customers to understand and adopt the product.

2. Data Requirements: AI products often require a significant amount of high-quality data to function effectively. Ensuring the product has access to such data, and that it can leverage it in a way that adds value for customers, is crucial to achieving PMF.

3. Regulatory Landscape: Depending on the industry, AI startups may face stringent regulatory requirements that can impact their product development and market strategies.

4. Evolving AI Capabilities: The rapid pace of AI development means that an AI startup's product capabilities can evolve quickly, which can shift its market fit.

When evaluating AI startups, investors must assess whether there's a strong product-market fit. This involves analyzing the startup's target market, understanding the specific problem the product solves, and gauging how well the product addresses this problem. Investors should also evaluate feedback from the startup's early customers and look for signs that the product is not just liked but loved.

Achieving PMF doesn't guarantee a startup's success, but it significantly increases its chances. It can be an excellent indicator that the startup is on a promising trajectory, making it an attractive opportunity for investors.

Strategies for analyzing the market potential of an AI product.

Investors need to adopt a systematic approach to assess the market potential of an AI product. Here are some strategies that can guide this analysis:

1. Understand the Problem: The first step is to thoroughly comprehend the problem the AI product is trying to solve. Is it a pressing issue? What is its impact? Is it widely recognized, or is it a latent need?

2. Define the Target Market: Determine the size and characteristics of the market that faces this problem. Who are the potential customers? What are their behaviors, preferences, and needs? Are they willing and able to pay for a solution?

3. Assess the Competition: Identify any existing solutions on the market. What are their strengths and weaknesses? How does the AI product differentiate itself? Remember, competition isn't just other AI startups; it could be traditional solutions or even the choice of potential customers to do nothing.

4. Test Market Demand: Look for evidence that the market wants the AI product. This could be feedback from early users, pilot studies, or market surveys. Is there a strong pull from the market for the product?

5. Consider Market Trends: Evaluate any relevant market trends. For example, if the AI product is in healthcare, how is the adoption of AI technology in the healthcare industry trending? Is the regulatory environment becoming more favorable or restrictive?

6. Estimate Market Penetration: Based on the above factors, make an informed estimate of the share of the market the AI product could realistically capture. This can guide your assessment of the product's revenue potential.

This market analysis is crucial in determining whether an AI product has a strong product-market fit. It provides investors with valuable insights into the product's potential for success and can highlight potential red flags or opportunities.

Case studies demonstrating the success of AI startups with strong product-market fit

In this section, we'll explore two case studies that illustrate the success of AI startups that have effectively found a strong product-market fit. We’ve seen these companies before, but they illustrate the topic well.

Case Study 1: PathAI

PathAI, a Boston-based startup, utilizes AI to aid pathologists in diagnosing diseases from pathology slides. The startup recognized a serious problem: human pathologists, despite their expertise, can still make mistakes in complex diagnoses, and there is a global shortage of pathologists to meet the demand. PathAI developed a solution that combines AI and machine learning with the expertise of pathologists to increase diagnostic accuracy and efficiency.

Through their market analysis, PathAI identified their target market as medical institutions worldwide, including hospitals and clinics, with a particular focus on regions with a shortage of pathologists. Despite competition from other AI-based diagnostic tools, PathAI has managed to differentiate itself by offering AI-powered solutions that enhance rather than replace the work of pathologists, thereby reducing resistance from the medical community.

The strong demand for PathAI's solution has been demonstrated through its partnerships with big pharmaceutical companies and medical institutions, and it continues to show rapid growth. PathAI's success underscores the importance of identifying a pressing problem, targeting an appropriate market, and differentiating the product from its competition.

Case Study 2: UiPath

UiPath, a startup based in New York, provides an AI-powered Robotic Process Automation (RPA) platform that automates mundane and repetitive tasks, freeing up employees to focus on more complex, value-added tasks. UiPath recognized that businesses across sectors were seeking ways to improve efficiency and reduce costs, and they developed an AI solution to meet this need.

Their market analysis defined their target market as businesses of all sizes across sectors looking to automate processes. Despite competition from other RPA platforms, UiPath has set itself apart through its user-friendly interface, scalability, and wide range of functionalities.

With a growing demand for automation in the business world, UiPath's solution has been widely adopted, with thousands of organizations, including government agencies and Fortune 500 companies, using its platform. The success of UiPath serves as another example of an AI startup achieving a strong product-market fit.

These case studies highlight how a strong product-market fit can lead to the success of AI startups. They underline the importance of understanding the problem, defining the target market, differentiating the product, and demonstrating market demand.

5. The Role of Intellectual Property in Assessment

The importance of considering intellectual property rights in AI startup assessment

As we delve deeper into the complexities of assessing AI startups, we encounter a crucial, yet often overlooked, facet – Intellectual Property (IP). When investing in startups, especially those in the AI realm, understanding the IP landscape is essential.

IP serves as an integral part of a startup's value proposition. It encompasses all the legally recognized exclusive rights to creations of the mind, whether it be inventions, designs, or even the name of the company itself. For an AI startup, this might include proprietary algorithms, unique datasets, patents, trade secrets, or software.

When it comes to AI startups, a well-defined IP strategy not only offers a competitive edge but also significantly increases the company's value. An AI startup with strong IP rights has a greater chance of securing funding, attracting partnerships, or even becoming an acquisition target. Moreover, it serves as a protective shield, safeguarding the startup's unique innovations from competitors.

However, the IP landscape in AI is complex. There are ongoing debates around the patentability of AI technologies and the ownership of AI-generated content. Therefore, as an investor, one must understand the AI-specific IP challenges and assess how the startup navigates them.

In the next sections, we will look at strategies for assessing the IP position of an AI startup, and how this understanding can contribute to informed investment decisions. This will include discussions around AI patents, trademarks, copyrights, and trade secrets, as well as legal considerations specific to AI, such as data privacy and ownership of AI-generated content.

Real-World Examples Illustrating the Impact of IP Considerations on Investment Decisions

Drawing from the world of AI startups, there have been several instances where IP considerations have played a pivotal role in investment decisions. Here are a few noteworthy examples:

Take the case of DeepMind, the London-based AI company acquired by Google in 2014. DeepMind is known for its cutting-edge research in AI and machine learning, often leading to the creation of valuable intellectual property. The value of its proprietary technology, protected by IP rights, was a key consideration in Google's decision to acquire the company for an estimated $500 million (in addition to their team).

Another striking example is the legal battle between Waymo (Alphabet's self-driving car project) and Uber. In 2017, Waymo sued Uber for allegedly stealing trade secrets related to its autonomous driving technology. The lawsuit highlighted the critical importance of IP in the competitive landscape of AI. It served as a stark reminder for investors about the potential risks associated with IP infringement and the importance of conducting a thorough IP due diligence.

There's also the case of BenevolentAI, a leading AI startup in the pharmaceutical industry. The company's unique selling point is its extensive AI patent portfolio, which includes AI algorithms that accelerate drug discovery. The robust IP portfolio has played a significant role in attracting over $200 million in funding.

These real-world examples underscore the profound impact that IP considerations can have on the valuation of AI startups and, consequently, on investment decisions. They highlight the importance of thorough IP due diligence and the need for investors to grasp the intricacies of IP rights and their implications in the context of AI technologies.

6. An Initial Look at Ethics in AI Startup Assessment

While this chapter has focused primarily on technical and market aspects of due diligence, it's worth noting that ethical considerations are becoming increasingly significant in the assessment of AI startups. As AI technologies continue to permeate various aspects of our lives, they raise complex ethical questions around data privacy, algorithmic bias, job displacement, and more.

Investors need to be mindful of these ethical implications because they can profoundly impact a startup's reputation, regulatory compliance, and ultimately its success. Startups that fail to address these issues may face backlash from consumers, regulatory penalties, or lawsuits, all of which could adversely affect the return on investment.

However, ethical considerations in AI investments are not just about risk mitigation. They also present opportunities. Companies that proactively address AI ethics can differentiate themselves in the market, attract customers, and even shape regulatory discussions in their favor.

This brief discussion serves as an introduction to the ethical considerations in AI startup assessment. We will delve deeper into this topic in a later chapter, where we will explore how to incorporate ethical considerations into investment decisions, the implications of AI ethics on startup valuation, and how investors can contribute to the responsible development and deployment of AI technologies.

7. Conclusion

Assessing AI startups requires more than just traditional due diligence. It's an intricate process that involves understanding the team's AI talent, determining the real value that the AI product brings, performing a detailed market analysis for product-market fit, and considering intellectual property rights. The rapidly evolving nature of AI startups adds an additional layer of complexity to this process.

The team's AI talent is instrumental in driving the success of the startup. The strength of this talent can be a key differentiator, setting the startup apart from its competition. However, assessing AI expertise is not without its challenges, given the technical nature of the field and the hype surrounding AI.

In evaluating the product, it's crucial to discern between genuine innovation and mere hype. While the market is buzzing with AI products, not all add significant value to their industries. Case studies can provide valuable insights into products that have managed to stand out and bring substantial value.

Understanding the concept of product-market fit in the context of AI startups is crucial. A strong product-market fit can be a key indicator of the startup's potential success. Again, case studies can provide lessons from startups that have successfully achieved a strong product-market fit.

Intellectual property rights can significantly influence investment decisions. They not only protect the AI startup's innovations but also add to its valuation, making it an attractive proposition for investors.

Lastly, while we've only touched on it briefly here, the role of ethics in assessing AI startups is becoming increasingly important. Ethical considerations can impact a startup's reputation, regulatory compliance, and even its market differentiation. A more in-depth discussion on this will follow in a later chapter.

As an investor, performing comprehensive due diligence can significantly increase the odds of making a successful investment in the vibrant and rapidly changing landscape of AI startups. The stakes are high, but so are the potential returns, making the journey an exhilarating one.