AI Startups Thrive When Times Get Tough: Why I’m Doubling Down on AI Investments
AI investments are at an all-time high. Despite all the doom and gloom in the startup market – talk of down-rounds, sinking valuations, and longer runways – one tech sector stands to weather the economic uncertainty ahead better than others: AI. As a venture capitalist for over 20 years, I’m bullish on AI startups, especially at the early stages, and plan to accelerate AI investments in the next 12-18 months. As businesses look to increase productivity, cut costs, and better serve their customers during tougher economic times, AI will see more adoption, not less.
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So, which sub-sectors of the AI industry hold the most promise for investors?
AI investments are very compelling. In general, any AI application or developer tool that enables companies to “do more with less” has a strong chance at gaining traction during a downturn. Here are three especially compelling areas.
Synthetic Data
Gathering, analyzing, and understanding data is critical for any company’s success, yet it’s also complex and time-consuming. There are regulatory hurdles to overcome, as well as simply finding the right tools and teams to process the truly vast amounts of data being created every second. Companies also lack the right kind of data; it can take years to collect a statistically-relevant data set. Synthetic data, which is created by AI algorithms rather than collected via real-word events, can help. We’ve seen companies use synthetic data to improve software development, speed up R&D, train machine learning models, and fine-tune product roadmaps. It’s already used in healthcare, finance, insurance, cybersecurity, manufacturing, and autonomous vehicle development, and will infiltrate every sector in the near future.
Robotics
Robots are efficient and tireless, helping companies achieve scale and improve profitability; that’s why it’s common to purchase more robots during lean times. But robots without AI are simply machines; it’s AI that gives them intelligence and autonomy. Thus, companies building AI to power robots hold outsized potential in the coming years. One example is FarmWise, a company my firm backed, which has built an AI platform combined with computer vision to distinguish weeds from crops.
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Tractors retrofitted with sensors, cameras, and this AI software become autonomous weeding machines that save farmers time and money – and use zero pesticides. But there are hundreds of examples of AI software making robots smarter, faster, and more precise.
Robotic Process Automation [RPA]
AI is, at its core, sophisticated code that can make other software better. Thus, I’m interested in backing startups building AI software that’s used to improve applications or workflows.
AI is already woven into software for hiring, financial robo-advising, insurance claims processing, office productivity, and much more. AI is also becoming more prevalent in healthcare, a sector that was hit especially hard by the pandemic. Doctors and nurses everywhere needed better, faster, more efficient digital tools to screen patients, make diagnoses, and generate treatment plans – and AI is beginning to fill those roles. We invested in Regard, a healthcare AI startup that scans millions of data points in de-identified patient records to help doctors create faster and more accurate diagnoses and treatment plans. There are dozens of healthcare AI startups making inroads in other areas of diagnostics, patient care, and drug discovery.
My prediction that AI startups will weather the upcoming storm better than other software companies is born out in some recent statistics. While overall venture investing has been trending down all year, AI investment has dropped less than other sectors. And companies plan to accelerate AI investments into smarter technologies over the next two years. While no one has a crystal ball, AI companies are well positioned to grow even despite the economic headwinds, quite simply because AI is designed to “do more with less.”
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