10 Hurdles of Building a Deep Tech Startup in the Age of ChatGPT
This article provides an overview of the unique challenges faced by deep tech startups, including technical complexity, regulatory hurdles, and financial risks.
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Deep tech startups face a distinctive set of challenges compared to other tech companies, making it essential for founders and investors to be prepared for a more complex and demanding journey.
AI and ML play a pivotal role in these companies, enabling them to analyze vast amounts of data, identify patterns, and develop advanced solutions while marketing is stepping aside.
Large language models (LLMs) such as GPT-4 are revolutionizing natural language processing, drug discovery, and personalized medicine. LLMs can also facilitate conversations with users, enabling healthcare providers to develop chatbots that offer mental health support, answer patients' questions, and even provide therapeutic interventions. By harnessing the power of AI, these cutting-edge technologies have the potential to transform the future of mental healthcare and improve the lives of millions of people worldwide.
After building my previous tech company, Bright Box, and selling it for $75M in 2017, I found myself in a different environment with my new company Brainify.ai, which aims to increase the likelihood of new drug approval by 80% and reduce R&D costs by utilizing AI/ML-driven EEG biomarker prediction, which is set to increase.
Deep tech startups often work on cutting-edge technologies that are scientifically complex and require a deep understanding of the underlying principles. This necessitates the involvement of experts with specialized knowledge in the field, which can be difficult to find and retain.
Long Development Cycles
Developing and validating new technologies can take a considerable amount of time. This may lead to longer development cycles and delayed market entry, which can be financially challenging, especially for startups with limited resources
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Due to the inherent uncertainties in scientific research and development, deep-tech startups often face a higher risk of failure. Investors may be more cautious about funding these ventures, making it harder for deep-tech startups to secure funding.
Deep tech startups may operate in highly regulated industries, such as healthcare, biotechnology, or energy. Navigating complex regulatory requirements and obtaining necessary approvals can be time-consuming and resource-intensive.
Intellectual property (IP) protection
Deep tech startups often rely on valuable IP to maintain their competitive advantage. Protecting this IP through patents and other legal mechanisms can be challenging, expensive, and critical to the startup's success.
Marketing Approach in Deep Tech Startups
As an experienced entrepreneur, one of the most challenging aspects of navigating a deep tech startup has been the difference in marketing approaches. In deep tech, a significant amount of time is spent in stealth mode, focusing on research and development before promoting any products or services. Unlike other industries, where marketing can begin early in the product development process, deep tech startups must exercise caution and ensure that any claims made are scientifically proven and validated.
This meant that I had to adopt a more conservative marketing approach, only promoting our offerings once we had achieved specific milestones or reached a particular threshold of confidence in our technology. It has been essential to maintaining our reputation in the scientific community and among potential investors and customers, as credibility is crucial in this field. Any premature or unsubstantiated claims could quickly damage our reputation and hinder our ability to succeed in the long run.
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Navigating this delicate balance between building excitement around our technology while ensuring that we only communicate accurate and verifiable information has been a particularly challenging aspect of running a deep tech startup.
The main differentiators for a deep-tech startup lie in the extensive research activities, high risk, and uncertainty associated with the initial stages of development. In the beginning, it is often unknown whether the scientific basis of the startup is even possible or not.
A High Risk of Scientific Failure
This contrasts with typical tech startups, where the focus is more on product-market fit and execution strategy. In other words, for tech startups, the emphasis is on how the business will be carried out rather than whether it is even feasible. Deep tech startups, on the other hand, face a high risk of scientific failure due to the inherent nature of research activities and the uncertainties involved.
Funding After Validation
Securing funding for our startup began with an initial investment of $250K from Mariam Khayaredinova (CEO & co-founder) and myself. We wanted to first validate the need for our solution and assess the possibilities of achieving our goals. Once we were confident in the potential of our idea, we decided to raise additional capital from angel investors. Leveraging my previous exit and our strong track record, we were able to secure around $1 million from angel investors and additional $350K from the founding team.
Currently, we are in the process of proving our market fit, demonstrating the scalability of our technology, and showing the potential for substantial returns on investment for our backers. It's crucial to stay up-to-date with the latest advancements and emerging opportunities. The deep tech field is constantly evolving, so being aware of both the challenges and possibilities is vital for success.
Ivan Mishanin is the co-founder and COO of Brainify.ai, an AI/ML biomarker platform for novel treatment development aimed at psychiatry. His previous tech company, Bright Box, was sold to Zurich Insurance Group for $75M.