The Trojan Notebook
The enterprise deal started in a grad school lab.
The electronic lab notebook market had been developing for more than two decades before Benchling launched in 2012. However, every product was built for the wrong user.
Benchling was built by the right user.
The Problem Was Not the Paper
Walk into a molecular biology lab and look at the bench. You will find pipettes, centrifuges, gel boxes, and freezers stacked with labeled samples. You will also find a notebook. Not a laptop. A physical notebook with numbered pages, bound so none can be removed, with entries written in ink. Next to every entry, a date and a signature. In many labs, a second signature from a witness.
This was not an artifact of old habits. It was a deliberate system, built around specific legal and regulatory requirements that made paper more defensible than software for most of the history of biomedical research.
The carbon copy lab notebook was the institution’s answer to two problems: intellectual property documentation and regulatory compliance. U.S. patent law required contemporaneous evidence of inventive activity. A properly maintained lab notebook, with bound and numbered pages that prevented insertions, ink entries that prevented erasure, and dated witness signatures that corroborated the sequence of work, was one of the strongest forms of that evidence. FDA regulations under 21 CFR Part 58, governing Good Laboratory Practice, required that all research data be recorded directly, promptly, and legibly in ink. The carbon copy gave researchers an immediate duplicate. One copy stayed in the lab. One copy went with the researcher. Neither could be altered without detection.
This is why paper persisted not just through the 1990s but through the 2000s and into the 2010s, long after the rest of the scientific world had adopted computers for everything else. The notebook was not a failure of imagination. It was an answer to a real problem: how do you create a trustworthy, tamper-evident, legally defensible record of the work that generates billions of dollars in drug development and intellectual property? For decades, paper solved that problem in a way that software did not.
As recently as 2012, the vast majority of academic scientists were still using paper lab notebooks. Most major research universities had no enterprise-wide electronic lab notebook solution at all. The software that existed to replace paper had not convinced most researchers it was worth the switch.
Sajith Wickramasekara, the founder of Benchling, understood why. He had used both.
Why the Software That Existed Didn’t Work
Electronic lab notebook (ELN) software was not new when Benchling launched. The market had been developing since the late 1980s. What it had been developing was the wrong product.
IDBS, founded in 1989, became the dominant incumbent in pharmaceutical and large biotech environments. Its E-WorkBook platform combined electronic lab notebook functionality with laboratory information management into a single large, configurable system built by database consultants for pharmaceutical IT departments. Implementing it required months of deployment, dedicated IT infrastructure, custom configuration, and ongoing IT support. It did not launch a cloud version until 2017, nearly three decades after founding.
It was built to satisfy compliance officers and IT directors, not to make a researcher’s workday faster.
This mattered because the lab notebook problem had two very different versions. The first was the pharma enterprise version: a large organization with IT departments, regulatory affairs staff, and existing vendor relationships needed a solution that satisfied 21 CFR Part 11 electronic records requirements, integrated with existing LIMS (Laboratory Information Management System) infrastructure, and generated audit trails that could survive FDA inspection. IDBS was built for that version of the problem.
The second was the version Wickramasekara had lived: a research lab at a university or an early-stage biotech, where the actual work of science was being done, where there was no IT department, no enterprise budget, and where a researcher trying to find data from three months ago was still searching through a paper notebook or an Excel file on someone’s laptop. For this version of the problem, the existing software was worse than paper. It required institutional procurement, IT setup, ongoing maintenance, and licensing fees that most academic labs simply could not afford.
When researchers did try legacy ELN systems, they encountered auto-logout sessions that timed out after fifteen minutes of inactivity, rich text editors that could not paste from Word documents, unfixed bugs that had been reported for months, and interfaces that looked and felt like database applications from the late 1990s.
LabArchives, which launched in 2009, was the closest prior attempt at the middle ground. It was cloud-based, designed for academic researchers, and did not require institutional IT infrastructure to deploy. It eventually reached more than 350,000 researchers at universities worldwide before being acquired by Dotmatics in 2021. But LabArchives spread primarily through institutional enterprise agreements: universities purchased licenses and researchers used it because it was available.
Benchling took a different approach. It spread because individual researchers chose it for work they were already doing. The distinction between institutional adoption and researcher-led adoption is not semantic. It produces fundamentally different products.
Scientists did not reject electronic lab notebooks because they preferred paper. They rejected the specific software that existed because adopting it meant trading a problem they had learned to manage for a different set of problems that were harder to manage. What Wickramasekara later characterized as roughly 40% of researcher time being lost to administrative work, data entry, and hunting through fragmented records was a cost they paid daily. An enterprise ELN with a six-month implementation timeline and a five-figure annual license was a cost they could not justify.
Nobody had built the product that sat between those two failure modes: modern, cloud-native, designed for the researcher rather than the IT director, and accessible enough that a graduate student could start using it today. That product required understanding both failure modes from the inside. Wickramasekara had lived in the second one.
The Man Behind the Bench and Screen
Sajith Wickramasekara grew up interested in both computers and science. He attended the North Carolina School of Science and Mathematics, a residential high school for students with particular aptitude in science and math. He arrived at MIT to study electrical engineering and computer science, but his interest in biology research pulled him toward the lab.
Before founding Benchling, he worked as a research assistant at Duke University Medical Center and then at Liquidia Technologies, a biotech in Durham building microfluidic drug delivery systems. These were working research environments with functioning wet labs: experiments, samples, notebooks, results that needed to be recorded, reproduced, and built upon. He used the tools that existed. He spent enough time inside those environments to understand them from the inside.
The contrast he kept returning to was specific. As a software engineer, he had version control. He had searchable documentation. He had real-time collaboration with teammates across different locations. He had infrastructure that evolved continuously and improved every year. In the biology lab, conducting work he considered more consequential than most software engineering projects, he had a paper notebook and Excel.
Most software entrepreneurs looked at biology labs as a niche market with complex procurement cycles and slow adoption rates. Most biologists had internalized the limitations of their tools as structural features of the work rather than solvable problems. Both were wrong, but neither had the vantage point to see it clearly.
Wickramasekara had both perspectives simultaneously. He knew the tools that existed in modern software development well enough to understand that what biology labs were running on was not just outdated but structurally broken. And he had spent enough time in those labs to understand exactly where it was broken: not in the compliance layer, not in the audit trail, but in the daily workflow of a researcher designing an experiment, running it, recording what happened, and trying to build on it the next day. He had been that researcher. He knew which parts were broken because he had experienced them breaking on him.
That combination was rare. Biology graduate students who spent their careers in wet labs had stopped seeing the paper notebook as a problem worth solving. Software engineers who had never spent time at a bench had no way to understand which features would actually matter. Wickramasekara sat at the intersection of those two populations, and he was young enough that the contrast between modern software tools and the state of lab infrastructure felt like an obvious failure rather than an accepted condition.
He dropped out of MIT and co-founded Benchling in 2012 with Ashu Singhal, whom he had met in his freshman year. Singhal brought the engineering depth the product would require. Wickramasekara was 21. Paul Graham accepted Benchling into Y Combinator’s Summer 2012 batch and offered a framing that captured the opportunity precisely:
“People who work with biology are important and so to be the most important software for them will surely turn out to be valuable.”
The First Product Was Not a Lab Notebook
Benchling’s initial release was not an electronic lab notebook. It was a free, web-based DNA sequence design and plasmid mapping tool.
Why? Because every molecular biology lab needed to design plasmids and sequences. Every researcher doing that work was using desktop applications that required installation, licensing, and a specific operating system, or older web tools that had not been updated in years. Benchling offered the same capability in a browser, immediately, for free. No institutional procurement. No IT approval. A graduate student could be using it in ten minutes.
This was the entire strategy in one product decision. Not an enterprise ELN sale to a pharma IT department. A free tool that researchers actually needed for their daily work, with zero friction to access. Once a lab adopted Benchling for sequence design, the workflow was already inside the door. The same platform that managed their sequences could manage the experiments that used them. The data that had lived in five disconnected places could live in one.
The free academic tier was not a loss leader with a later plan to charge. It was the distribution mechanism. Wickramasekara understood something the incumbent ELN vendors had never needed to think about: the graduate students and postdocs using the product today were the scientists entering pharma and biotech companies in two to five years. They would bring their workflows with them.
The academic base was the sales force.
Legacy vendors had approached the market from the top down, selling to pharmaceutical IT departments with enterprise contracts. Benchling approached it from the bottom up, earning the trust of researchers before they had any purchasing authority, so that when they did, Benchling was already the tool they knew.
By 2015, more than 6,000 scientists across 1,000 research institutions were using Benchling. Andreessen Horowitz led a $5 million Seed round, with Thrive Capital and SV Angel also participating. The investment thesis, as Balaji Srinivasan wrote at the time:
“Software may be eating the rest of the world, but it has failed science.”
Eighteen months later, Thrive returned to lead the $7 million Series A: a signal that the academic-to-industry pipeline was already working.
The View From the Bench Was the Edge
The pharmaceutical and biotech industry adopted Benchling faster than it had adopted anything the ELN market had previously produced because the product was designed from the researcher’s seat outward, not from the compliance requirement inward.
Wickramasekara could see things that the database consultants who built IDBS in 1989 could not. He understood that what he had characterized as roughly 40% of researcher time being lost to administrative work was not an abstraction. He had experienced it. He understood that compliance requirements were real but secondary for most research workflows, and that building compliance-first produced a product that was effectively useless for the vast majority of researchers not yet in a regulated development stage. He understood that the obstacle to cloud adoption in labs was not a genuine security concern but institutional inertia, and that a tool built for individual researchers would bypass institutional purchasing cycles entirely.
He also understood something that did not surface in any sales conversation with an IT director. The fragmentation of lab data was not just an inconvenience. It was a structural contributor to the reproducibility crisis running through biomedical science.
A 2016 Nature survey of more than 1,500 researchers found that 70% had failed to reproduce at least one peer’s study, and more than 50% had failed to reproduce one of their own experiments. When experimental conditions lived in personal notebooks, when the chain of custody between a biological material and the results it produced was not tracked, when a researcher left a lab and took their notebook with them, the work they had done became effectively unreproducible. One study, published in the journal Molecular Brain, found that when editors requested raw data from submitted manuscripts, more than 97% of authors did not provide it when asked. While narrow in scope, the finding pointed at a structural pattern across biomedical research.
Benchling addressed this not by selling reproducibility as a compliance argument but by making the research workflow itself better. The version control, the linked records, the searchable history: these were product decisions that made daily research faster and easier.
The reproducibility benefit was a consequence of building the right product, not a feature pitch.
That distinction is only available to someone who had done the work. The compliance-first approach produced products that researchers avoided. The workflow-first approach produced a product they actually used, which also happened to address the reproducibility problem.
When You Build for the Right User
In 2017, five years after launch, Benchling introduced the Bioregistry and Inventory modules, connecting physical biological materials, plasmids, strains, antibodies, cell lines, to the experimental records that used them. The expansion followed the same logic as the original product: researchers working with sequences were also working with physical samples, and the connection between the two was entirely unmanaged. The product told you what was missing, if you had spent enough time in the lab to understand what you were seeing.
The valuation trajectory reflects how completely the market validated that logic. From $60.5 million pre-money at Series B in April 2018 to $3.8 billion pre-money at Series E three years later, the company’s growth rate was among the sharpest in life sciences software of that era. The category it had entered had existed for decades. What had not existed was a product researchers would actually choose to use.
In January 2026, Benchling announced a partnership with Eli Lilly’s TuneLab to embed Lilly’s proprietary machine learning models, trained on more than a billion dollars of research data, directly into the platform. The product has moved from a research notebook to an AI-augmented R&D infrastructure. The orientation has not changed. The scientist is still the user the product is designed for.
The full arc of that progress, across fourteen years of company-building, is below.
Key Metrics (2024)
Customers: 1,300+ biotech companies; more than half of the top 50 global biopharma
Scientists: 200,000+ active users globally
Revenue mix: 70%+ enterprise; estimated $200M+ ARR
Average ACV: ~$175K blended across all tiers (Sacra, May 2024); enterprise plans range to $1M+ for largest customers
Total raised: $417.88M across 8 rounds (per PitchBook)
Peak valuation: ~$6.1B post-money (Series F, October 2021)
Where the Moat Actually Came From
The electronic lab notebook market had incumbents with enterprise relationships, deep compliance infrastructure, and in IDBS’s case more than two decades of pharmaceutical deployment experience when Benchling launched. None of that protected them from a 21-year-old Electrical Engineering and Computer Science (EECS) student who had spent time at a research bench.
The protection the incumbents had was real: long contracts, switching costs, regulatory compliance depth, and relationships with pharma IT departments.
What they did not have was a product built for the person actually using it.
Every feature decision in every incumbent product had been made by someone who understood the purchasing side of the market. None of them had written entries in a carbon copy notebook, searched for an experiment result in an Excel file from eight months ago, or experienced the specific daily friction of conducting important research on infrastructure that had not changed since the 1990s.
Wickramasekara was not a biologist who learned to code. He was an engineer and computer scientist who had worked in biology labs, which gave him something neither category had on its own: he could see the researcher’s experience with precision, and he had the technical background to understand what building something better would require. The buyer-builder credential is not a single observation. It is the accumulation of enough time inside a broken system to understand where it is actually breaking, combined with the specific knowledge to do something about it.
That is what the incumbents could not replicate. The ELN vendors who had been selling to pharmaceutical IT departments for two decades had deep relationships with the people who controlled the budget. What they had never built was the product the person at the bench would actually choose to use. Benchling built that product first because Wickramasekara had been that person.
Why We Like the Life Sciences Industry
As a biology major at Rice, I used carbon copy notebooks. I hated writing in them. I always thought it was so tedious and made the labs twice as long as they needed to be.
But I understood why they existed. As an college student, it was to prevent plagarism. In a professional context, it was to ensure compliance and traceability. A properly maintained carbon copy notebook is legally defensible in a way that a digital file on someone's laptop is not. That is not bureaucratic inertia.
It is the product of decades of patent litigation and FDA enforcement shaping how research institutions protect their work. That experience made Wickramasekara's insight immediately legible: the compliance requirement was legitimate, but it had become an excuse to build products that served the institution's legal department and nobody else in the lab.
The ELN vendors building from the top down were answering the right question for the wrong customer. Their question was: how do we satisfy audit requirements and enterprise compliance at the C-Suite level? That is a legitimate problem. It produced real products with real enterprise contracts. What it did not produce was anything a graduate student at a bench would choose to use voluntarily.
Benchling answered a different question: how do we make the daily workflow of the researcher better than paper? Those two questions look like they are competing in the same market. They are not. They serve different customers entirely. The top-down compliance vendors were selling to the executive managing regulatory exposure. Benchling was earning the trust of the person doing the actual work before anyone in that organization had a procurement decision to make. By the time those researchers moved into industry and their companies needed an ELN, the answer was already the tool they knew.
The bottom-up approach built continuity and traceability into the workflow itself rather than bolting it on for compliance. That is a structural difference in product philosophy, and it produced a structural difference in adoption. The researcher did not need to learn a compliance system. The compliance value was embedded in the workflow, not added on top of it.
Life sciences is a space Daring invests in directly, and the reason is exactly the pattern Benchling demonstrates. Regulation runs through the entire industry, not just the research bench. It governs how data gets recorded in the lab, how products get approved, and how they get sold: sales reps operating under FDA promotion guidelines, training programs built around compliance documentation, commercial teams managing audit requirements alongside revenue targets. The same structural gap exists at every layer.
PraxisPro, one of our portfolio companies, operates in this space. Different part of the industry, same dynamic: compliance requirements that shaped how the software got built, and practitioners left working around tools that were never designed for them.
The founders who will move regulated markets are the ones who stopped accepting the friction as a fixed cost and started seeing it as the product. That is the credential Daring looks for. Not the market analysis. The lived experience of the broken tool.