In 2011, famous venture capitalist Marc Andreessen made a bold claim: software is eating the world. In a widely cited article, he described how programming tools and internet-based services reduce the cost of building software. This has in turn revolutionized industries like entertainment (Netflix) and retail (Amazon). In fact, Andreesen argued that this trend will affect every industry. Nine years later, 7 of the top 10 largest public companies in the world are software companies. It’s safe to say that Andreessen was right.
But he wasn’t 100% right.
My experiences over the past four years as BenchSci’s CEO and cofounder have proven to me that software’s diet isn’t balanced. Yes, it’s eaten much of the world. But it has missed some of society’s most important work. Bench scientists doing preclinical R&D for diseases like COVID-19 haven’t benefited. Rather, they struggle with underfunded, underpowered, outdated, and clunky software tools. These hinder productivity and slow the pace of new discoveries. And that hurts all of us.
Over the past few years, I’ve discussed this problem with hundreds of pharma R&D stakeholders. These include biomedical scientists; pharma science, IT, and procurement leaders; the world's leading venture capitalists; specialized pharma R&D consultants from the top five consultancies; and CEOs of other biomedical software startups. My experiences and their insights have given me a unique perspective on what’s happening, why it’s happening, and how it impacts society. I’ll be sharing these insights in a three-part series, beginning with this article on what’s happening.
People familiar with preclinical R&D might dispute my claim. They might point to digital content platforms for scientists, from publishers like Elsevier. They might point to analysis tools that help make sense of data from lab machines. They might even point to websites that help scientists buy products from multiple vendors. Yes, these are information technology. But they're not productivity tools that help scientists run more successful experiments. And many are outdated, with dot-com era interfaces.
Compare this lack of productivity software support to any other industry. Imagine, for example, salespeople without a CRM. Accountants without bookkeeping software. Writers without word processors. Designers without Photoshop. Engineers without integrated development environments. And I could go on.
The one exception that proves the rule is electronic lab notebooks (ELNs). ELNs enable scientists to record their research, experiments, and findings digitally. Before them, scientists did so in a physical notebook.
Until recently, as most bench scientists will tell you, ELNs were no match for sophisticated productivity software in other industries. Since the ELN market is small (around $550M a year), it’s less competitive, which means it lacks innovation. So most ELNs have outdated interfaces, looking like they were built in the 2000s (which they were), and outdated data storage and analysis capabilities, with no cloud access.
This was the opportunity that SaaS ELN provider Benchling seized. Benchling is one of the few life science software success stories in the past 20 years. It revolutionized the market by transforming the ELN from a recordkeeping system to a full suite of software products that augment scientists’ workflow and elevate their productivity.
But I’ll be surprised if you can name another preclinical R&D success story like Benchling. Despite four years of research into the market, I sure can’t.
This isn’t to say that software hasn’t eaten parts of the life science industry. It has.
Just not preclinical R&D.
There are multibillion dollar software companies that support clinical trials and commercialization, such as Veeva. But apart from Benchling, there are few successful software companies that support preclinical R&D. As a result, when you sell software for preclinical R&D you quickly learn that these IT budgets are very small compared to those for clinical and commercial activity.
You would think pharma’s growing focus on digital transformation would improve the situation. But it hasn’t. There have indeed been an increase in the number of Chief Data Officers and Chief Digital Officers. And they often receive significant resources, with annual budgets upwards of $50M, to invest in digital innovation. But most of those investments aren’t in software to improve the productivity of preclinical scientists. Rather, they usually go to enterprise-wide data consolidation and analysis projects, clinical trial optimization projects, and digital health initiatives. Are these important? Certainly. Are they so much more important than empowering scientists with better software tools that the latter receives virtually no investment? Doubtful.
Disinterest in productivity software for bench scientists extends to the venture capital community.
Over the past few years, venture capitalists have invested $5.2B in AI for drug discovery across over 230 new ventures. These investments are primarily focused on generating novel drugs, repurposing existing drugs, optimizing clinical trials, and analyzing real world evidence. Most investment has gone into AI-driven biotechs such as Recursion, Insitro, and Benevolent AI. Why so little investment in AI to empower preclinical scientists?
In other industries, we’ve seen the emergence of a new breed of AI-powered productivity software that leverages “Coaching Networks.” Coaching Networks use data and machine learning to improve the performance of employees executing the most complex tasks. For example, Textio uses job description data and machine learning to coach human resource professionals in writing more effective job ads. But over the last four years, having met hundreds of investors as CEO of BenchSci, only a few are interested in funding such empowerment for bench scientists.
In fact, outside of AI for drug discovery, only four software companies in preclinical R&D apart from Benchling received significant funding. And they are all marketplaces: Quartzy, ZAGENO, Scientist.com, and Science Exchange.
Of course, I’m hopeful that this can change. I’m hopeful that investment in software to empower bench scientists will increase, contributing to new discoveries and treatments for society. We’ve built a company to be part of this movement, and have been fortunate to attract funding from progressive investors who share our vision.
But if we want to change the situation, we first need to understand why it’s happening. And that’s the focus of my next article in this series.