On the long path to success, a fork in the road many startups face is securing the right investment at the right time. We believe the next trillion dollar companies come from the deep tech themes of Human and Planetary Health. The goal of this post is to share perspectives on the capital needs of deep tech companies and to share learnings from how we at Freeflow helped two of our portfolio companies raise successful A rounds.

Early capital needs of deep tech companies are rapidly decreasing.

Conventional wisdom says that deep tech companies need significant capital to get going. That was historically accurate. However, that is no longer true. The broad-based application of machine learning and artificial intelligence has enabled many of them to be much more efficient, “smarter” about their work, which has resulted in an exponential decrease in the capital needs of many deep tech companies at the earliest stages of investment. This allows them to quickly get into the market and generate early revenues through sales and partnerships.

The companies are harnessing broad-based technologies to move faster and more efficiently:

  • Automation: By using computer algorithms and machine learning techniques, it is possible to automate many tasks that would otherwise require human labor. This can help reduce labor costs and increase efficiency, allowing deep tech companies to do more with less capital.
  • Cloud computing: Instead of investing in expensive hardware and infrastructure, deep tech companies can use cloud computing services to access the computing resources they need on an as-needed basis. This can help reduce upfront capital costs and make it easier for small companies to compete with larger ones.
  • Open source software: Many deep tech companies rely on specialized software tools and libraries. By using open source software, they can access a wide range of high-quality tools at no cost, reducing their need for capital to fund software development.
  • Collaboration and sharing: By collaborating with other companies and organizations, deep tech companies can share resources and knowledge, reducing the amount of capital they need to invest in R&D.

The specific techniques and technologies used for these purposes have evolved over time as advances in computer science have been made. The use of computer science for these purposes has increased in recent years due to a number of factors. For example, the widespread availability and increasing affordability of cloud computing services has made it easier for deep tech companies to access the resources they need without a significant upfront investment in hardware and infrastructure. Additionally, the development of new machine learning and artificial intelligence techniques has increased the potential for automation, further reducing the need for labor and capital. We believe that the use of computer science to reduce the amount of capital needed for deep tech companies will continue to increase in importance and impact as new technologies and approaches are developed.

Conventional wisdom also says that deep tech startups do not earn revenue quickly and it takes a lot of time and money to get to any sort of revenue. We find this to not be true. For example, out of our 27 companies, 12 of them have revenues and one of them is profitable. This is very rare for deep tech companies and a testament to our founders’ focus on product-market fit and the importance of generating revenues.

Entos: $3M Seed to $53M Series A in 15 months based on its AI/ML drug discovery platform

Freeflow invested $500K in a seed round of $3M for Entos in April 2020 along with lead investor Nexus Venture Partners. Initially, Entos built an AI drug discovery platform focused on customers in the biopharma industry because that showed the most potential for the company over time. Large biopharma companies used the company’s technology as part of their molecular discovery efforts with success. The company was able to complement the dilutive funding with ~$3M of non-dilutive funding, e.g. grants. When it came time for the next round of financing, the company then had reference customers that could speak to the specialness of the platform and how they’d used the technology. We worked closely with the company as they prepared for their Series A to refine the investor presentation and data room. We also helped develop a list of potential investors, many of whom we knew and helped facilitate introductions and build the investor syndicate. The company closed a $53M round in July ‘21 led by Coatue and Catalio with participation from OrbiMed and Sequoia Capital as well as existing investors Nexus Ventures and Freeflow. They also completed a $20M A+ round in October ‘22 from existing and new investors.

If we take a look at how the macro trends in computer science impact some of our human health portfolio companies, like Entos, we find good tailwinds in the overall time to market and economics:

  • Data analysis: Drug discovery often involves the analysis of large amounts of data from a variety of sources, including genetic data, clinical trial data, and chemical and biological data. By using advanced data analysis techniques, such as machine learning and artificial intelligence, drug discovery startups can more efficiently process and analyze this data, reducing the need for labor and capital.
  • Virtual screening: Virtual screening is the process of using computer algorithms to identify potential drug candidates from a large library of compounds. This can help drug discovery startups reduce the number of compounds that need to be synthesized and tested in the lab, saving time and money.
  • AI-driven drug development: AI technologies and molecular modeling enable computers to predict the properties of candidate drug molecules and how they might interact with biological targets. This can help drug discovery startups identify and optimize promising drug candidates more efficiently, reducing the need for costly and time-consuming laboratory testing.
  • Collaboration and sharing: By collaborating with other companies and organizations, drug discovery startups can access shared resources and expertise, reducing the amount of capital they need to invest in R&D.

Hydrosat: $4.5M Seed to $15M Series A in two years based on its Earth “heat map” data visualization platform

Freeflow invested $500K in a seed round of $4.5M in April 2021 along with lead investor Cultivation Capital in Hydrosat, a company that is building a data platform to provide “heat map” and earth ground temperature data based on unique thermal infrared sensors. The core technology was developed at JPL.

Fundamental to the company’s strategy is getting the sensors placed onto satellites in low earth orbit to capture earth ground temperature data. This information can then be sold to interested parties in its raw form or it can be further refined according to crop in order to provide a crop forecast which can then be sold to interested parties like large agricultural and financial companies.

If we take a look at how larger trends in computer science impact a company like Hydrosat, we find it has impacted the cost structure and speed to market in several ways:

  • Automation: By using computer algorithms and machine learning techniques, it is possible to automate many tasks related to space exploration and satellite operations. This can include tasks such as data analysis, image or signal processing, and spacecraft navigation. By automating these tasks, space startups can reduce labor costs and increase efficiency, allowing them to do more with less capital.
  • Simulation: Computer simulations can be used to test and optimize the design of spacecraft, payloads, and other space-related systems. This can help space startups reduce the need for costly and time-consuming physical testing, saving money and allowing them to bring products to market more quickly.
  • Collaboration and sharing: By collaborating with other companies and organizations, space startups can access shared resources and expertise, reducing the amount of capital they need to invest in R&D.
  • Smaller satellites: The use of smaller satellites, which can be built and launched more cheaply than traditional satellites, is becoming increasingly common in the space industry. By using small satellites, space startups can access space-based capabilities at a lower cost, reducing the amount of capital they need to invest in hardware and infrastructure.

For deep tech startups, computer science breakthroughs from a decade ago are now accelerating the growth velocity of breakthrough innovations. For this sector, the overall effect of reduced startup hard costs have changed the game. The startups we’re working with out of Caltech are able to get to revenue, get to market and get to scale faster and more efficiently than ever. Ultimately this will mean more solutions to global problems connected to Human and Planetary Health.