Dec 12, 2024, 11:00 AM ETInterview, Fireside Chat

Mastering Your Data with Sajith Wickramasekara

The speaker characterizes the 21st century as the "century of biology," driven by the compounding ability to read, write, and edit biological data.
Three primary shifts are projected for the century: unraveling biological mysteries, accelerating human health solutions, and reshaping the world through biological tools.
Biotechnology tools have evolved from "sledgehammers" to "scalpels," indicating a shift toward surgical precision in biological intervention.
The industry has transitioned from chemistry-based pipelines (small molecules) to biologics and advanced modalities (cell/gene therapies, CRISPR).
In 2012, Benchling was founded before approved cell/gene therapies existed and before CRISPR became a mainstream tool.

Benchling now serves over 200,000 scientists across 32 countries and supports 1,200 biotech and biopharma companies.
The company has achieved over $200 million in annualized revenue with 800 employees, 40 of whom work in R&D.
Benchling allocates approximately $100 million annually to R&D to maintain a scalable platform for cutting-edge science.
The company's origin story involves a software engineer building tools to replace paper notebooks and 1995-era software, starting with a free model for academics.
The business model shifted from a "productivity tool" for labs to an essential "front door" for data aggregation in biopharma, driving the move from zero to one million in ARR.

AI is projected to increase the volume of high-quality therapeutic molecule ideas by 10x.
Beyond target identification, AI offers significant potential to reduce "toil" in the latter stages of drug discovery, including animal studies, manufacturing scale-up, and process engineering.
The primary barrier to effective bio-AI is the lack of organized, structured data; many companies struggle with complex, multidimensional data models rather than just scale.
Sanofi, a $150 billion market cap company, is cited as a major example of a firm undertaking a multi-year, multi-team digital transformation to power AI at scale.
Significant "dark data" remains unstructured, particularly in vivo data from expensive studies (e.g., non-human primates), which often exists only in PDFs requiring manual error checking.
Benchmarking indicates that 60% of laboratory instruments remain unconnected, creating a bottleneck for automated data capture.

Going from zero to one was described as 100 times harder than scaling from one to ten, primarily due to the difficulty of articulating the value of structured data to enterprise buyers.
Early adopters were often academics with no willingness or ability to pay; the strategy relied on organic growth and "hill climbing" to reach 100 active daily users before monetizing.
The first major biopharma deal was lost twice; the company eventually won the customer after demonstrating a steep growth curve and the ability to handle complex data models.
Scaling required a shift from a "Lego brick" approach (highly customizable) to providing industry-standard best practices and opinionated guidance on data modeling.
Hiring deep specialists improves empathy and adoption but creates a challenge in unifying customer interactions across a fragmented team.
The founder notes that vertical markets are less competitive than horizontal ones, which can lead to unfocused efforts, requiring constant customer feedback to maintain direction.

The next 12 years of Benchling's strategy focus on helping customers derive value from the data they have already aggregated, rather than just digitizing it.
The roadmap aims to expand capabilities from research and development into manufacturing to enable faster time-to-market for life-transforming medicines.
The long-term vision involves creating a unified ecosystem where data flows from the molecule design through to manufacturing to optimize drug approval processes.