Seven Reasons Why AI Startups Fail
I consider Why Startups Fail by Tom Eisenmann, professor at Harvard Business School, one of the top five books written about startups and entrepreneurship. His work stands out as the definitive guide on understanding the reasons behind startup failures and how to build a successful one.
In addition to the universal reason – running out of cash – Professor Eisenmann identifies the 6 key reasons responsible for startup failures: team misalignments ("Bad Bedfellows"), rushing to market without proper validation ("False Starts"), overpromising and underdelivering ("False Promises"), growing too quickly ("Speed Traps"), poor hiring practices (“Help Wanted”) and pursuing unrealistic goals ("Cascading Miracles"). His thorough analysis provides a roadmap for entrepreneurs, particularly those in AI startups, to navigate these pitfalls and improve their chances of success.
A journeyman’s observation: Many AI startups fail, some quietly and others with significant notoriety.
While definitive data on the long-term failure rate of AI startups is still limited, it is reasonable to suggest that their failure rate may be higher than that of SaaS or general software startups, potentially nearing 90%.
Drawing from personal experience and industry insights, there are seven factors that either expand on Tom's identified causes for startup failure or introduce new challenges unique to AI startups:
Positioning Challenges. AI startups often encounter significant difficulties when they fail to clearly define their target market, ideal customer profile, and unique product value. This issue is prevalent in nearly every technology startup, but it is especially pronounced in AI companies. This is due to several factors, including the lack of clear definitions for some AI categories (e.g., infrastructure), ambiguous AI responsibilities for personnel within enterprises, inconsistent agreement on the classification of solutions, among other reasons. To overcome these challenges, it's crucial to first describe “What is it?” and build from that foundation.
One spectacular AI failure involving product definition is IBM Watson for Oncology. IBM Watson was initially marketed as a revolutionary AI system capable of providing personalized cancer treatment recommendations by analyzing vast amounts of medical data. However, the AI tool often provided recommendations that were deemed unsafe and inaccurate by oncologists, leading to its failure in gaining widespread adoption in the medical community. The misalignment between the AI's capabilities and the real-world needs of healthcare professionals highlighted significant gaps in its product definition and positioning. (This was both a positioning failure and technical failure.)
No Differentiation, Competitive Advantage or Defensive Moat. Many AI startups struggle due to a lack of differentiation, competitive advantage, or a defensive moat. These technology and market-related factors are interconnected and often arise from insufficient or poorly informed market research conducted by founders in the early stages. Additionally, without unique features or capabilities that set them apart, they often find it challenging to compete against other entrants in the market with similar offerings. Also, the rapid pace of technological advancements in AI means that maintaining a competitive edge is increasingly difficult. Startups that fail to establish a strong, defensible position in the market – a leading reason venture capitalists (VCs) cite for not funding the startup – can quickly become outmoded, overshadowed by competitors who innovate more effectively or leverage superior resources.
Riding the Wave. Riding the wave of excitement following the release of ChatGPT and subsequent versions, or other generative AIs, many startups hastily entered the generative AI market with a flurry of applications, sexy demos and quick technology fixes designed to capitalize on the market momentum. While this initial surge can generate buzz and early traction, it is often short-lived. Hastily developed products lack the robustness required for enterprise and discerning consumer use. As a result, they fail to deliver sustainable value, leading to enterprise lack-of-interest, customer dissatisfaction and eventual shutdown of the company. This highlights the importance of thorough development and market readiness over simply capitalizing on the latest trends.
The rapid evolution of AI design patterns for building applications with AI means a tool developed today could be obsolete in six months, making it difficult to build a company around a specific framework.
For enterprises and VCs, ensuring a product is unique rather than just a feature is crucial for fostering a viable company, as established leaders may quickly subsume the feature and render new startups irrelevant.
Delivering Sizzle rather than Steak. This is a longstanding issue in the software industry and distinct from point #3 (above). Many failed AI startups produced flashy demos that generate media buzz but fail to solve real problems.
Quixey secured $165 million in funding from Alibaba for an AI-driven search engine for apps. It billed itself as “AI + Search = Instant Actions”. When that did not work, it pivoted to the digital personal assistant space. Competition (Google and Apple) and business mismanagement challenges resulted in the company going out of business.
Humane has raised a total of $241 million in funding to date and launched an AI-powered wearable device that acted as a personal assistant. The company, founded by former Apple employees Imran Chaudhri and Bethany Bongiorno, secured its latest round of $100 million in March 2023. The company is currently exploring potential buyers due to the product's mixed reception and technical issues, mainly it faced significant criticism due to poor battery life, overheating issues, and software shortcomings.
Customers are Nebulous (yes, this is their nature). While AI innovations show great promise in the lab during the research phase, they often encounter significant challenges when implemented in the real-world. This failure includes instances of trouble meeting expectations and nebulous expectations on the part of customers.
Three examples illustrate these issues:
Babylon Health failed to meet market expectations with its AI diagnostic technology, which it claimed could outperform doctors.
IBM’s Watson Health struggled with doctors who cited technical issues related to integration, scalability, and reliability.
DeepScribe faced technical problems that led to errors in medical reports, such as incorrect terminology and inaccurate medication records, which posed significant risks in healthcare settings.
These cases highlight the critical gap between laboratory success and practical application, underscoring the need for rigorous real-world testing, market and hands-on customer validation of AI innovations.
Safeguards, Data Bias & Model Issues. Many AI product teams, in their zeal to rush AI technologies to market, cut corners on essential safeguards. AI startups often grapple with inherent biases in their training data and models. If these issues remain unaddressed, the resulting AI systems can be inaccurate and biased, leading to consumer rejection, competitive disadvantage, and a tarnished reputation as unreliable solutions. Many of these instances are not made public.
Included here are two public instances by companies that are not exactly startups. Google Gemini, for example, launched its new image generation feature, it predominantly produced AI-generated images featuring people of color, highlighting issues with data bias. OpenAI has faced challenges with safeguards, data bias, and model issues, highlighting the need for robust measures to ensure their AI systems are accurate, fair, and reliable.
VC Funding Pitfalls. Raising substantial venture capital can often be a double-edged sword for startups. It can help address the cost of AI talent and compute costs, but there is another edge.
Olive AI, for instance, secured a total of $856.3 million in funding across nine rounds. Despite this significant financial backing, the company struggled with a toxic culture, poor leadership, lack of focus, and the allure of "easy" VC money, ultimately leading to its downfall due to pervasive lack of accountability.
Similarly, Inflection AI raised an impressive $1.525 billion, including a $1.3 billion round in early 2022 from prominent investors like Microsoft, Reid Hoffman, Bill Gates, Eric Schmidt, and NVIDIA. Recently, Microsoft acquired most of Inflection AI's staff, including its co-founders Mustafa Suleyman and Karén Simonyan, and paid $650 million to license Inflection's AI models. Inflection AI continues to operate and focus on its AI studio business, building and testing custom generative AI models, positioning this transition as a “pivot” rather than a complete shutdown. These cases illustrate how substantial VC funding, while beneficial, can also lead to significant challenges and necessitate strategic pivots.
The failures of AI startups outlined in points #1 through #4 — related to Professor Eisenmann’s false starts and promises — revolve around a common issue: a flawed customer value proposition (CVP). Additionally, points #5 through #7 above emphasize the significance of customer relationships, establishing a solid foundation of AI values, and the approach to funding. These challenges underscore the vital importance of a comprehensive strategy for product-market fit, customer value proposition (CVP), company culture, and financing strategy. Many early-stage AI startups lack this comprehensive strategy, increasing the probability of failure.
AI startups face numerous challenges, including positioning, differentiation, and addressing customer expectations, that extend beyond the universal struggles of all startups. Learning from past failures and focusing on market validation, strategic focus, and operational excellence are crucial for translating AI's vast promise into practical, reliable, and valuable solutions.
I’d like to extend my thanks to Tom for reviewing this post before its publication, and Bob Mason of Argon Ventures for the review and additional points.