Why streamlining labs should be the Future of Scientific Innovation
And how we can create new funding models in the process
In order to sustain long term, I think that research labs must adopt systems thinking, standardization, and operational efficiency, and not just for better science, but to become investable, scalable means of innovation.
What is the problem?
Imagine two teams working on the same groundbreaking idea. One is a university lab, fueled by grants, driven by grad students, and managed by sheer goodwill and the other is a lean startup with onboarding systems, clear protocols, and deadlines that actually mean something.
Guess who files the patent first? Who secures funding to build the real-world application?
Academic labs are where innovation starts, but rarely where it scales. The reason isn’t a lack of talent or ideas, but a lack of systems.
If labs operated with even a fraction of the operational rigor startups rely on, they wouldn’t just do better science. They’d attract new capital, accelerate discovery, and become launchpads for real-world impact.
Why do we not solve it?
Let’s be honest. Most labs are held together by informal mentorship, outdated Word documents, and a few overworked senior people.
Training is inconsistent and often undocumented
Protocols live in someone’s google drive, or worse, their head
New lab members flounder for weeks before they can contribute
Lab productivity hinges on a handful of key players (lab techs and managers)
Reproducibility is a hope, not a habit
A staggering 50–90% of research is considered irreproducible, costing an estimated $28 billion per year in the U.S. alone (Freedman et al., 2015).
That’s not a funding problem, it’s a system problem.
We’re paying the cost
When labs don’t have systems in place, the costs pile up:
Experiments are repeated needlessly
Reagents expire before use
Students drop out or leave without transferring knowledge
Patents and publications get delayed
Startups never spin out—not because the science isn’t good, but because the workflows are chaos
If your lab can’t train someone in less than a month or replicate its own protocols, why would an investor or industry partner trust you to scale a discovery?
Archaic academic mindset hates industry and its ways of working
In contrast, biotech startups and CROs (contract research organizations) have to get organized, or go out of business.
Every process is documented and reviewed
New hires are productive within days
Training is tracked and outcomes are defined
Reproducibility isn’t a nice-to-have—it’s a non-negotiable
And because the risk is visible and managed, capital flows more easily
That’s what makes them fundable. And that’s the level of clarity and trust labs need to cultivate if they want to translate science into scalable solutions.
What do we need to be efficient without losing the academic mission ?
Labs don’t need to become companies. But they do need structure.
Here’s what that could look like:
Centralized Knowledge-
Stop relying on tribal memory. Use a shared digital platform to store protocols, safety guidelines, and troubleshooting notes.
Operational guide-
Have a real onboarding checklist. Define what “productive” looks like at week 1, month 1, and month 3.
Training Pipelines-
Map out key lab skills, link them to mentors or modules, and track who’s trained in what.
Outcome-Oriented Culture
Hold weekly lab meetings with defined goals. Include “reproducibility checks” before publishing or patenting. Emphasize and practice repeatability
What if we could see labs as operationally investable engines?
When labs start thinking like systems, the benefits extend beyond science:
Better internal communication
Faster onboarding of new members
Cleaner, reproducible data
Stronger partnerships with industry
New funding opportunities from translational incubators/accelerators, philanthropy, or venture teams
What new capital models could follow?
What happens when labs become operationally sound and results driven?
Innovation grants tied to training and reproducibility benchmarks
University venture funds that back labs like startups
Philanthropic/Venture capital aimed at labs with track records of translation- like Lux Capital does
Industry-Academia partnerships engrained within the research lifecyle, making most of this research translational and scalable
As more institutions look for impact beyond publications, these models will become the new normal.
Who is doing it right- George Church’s Lab at Harvard
If you want proof that this model works, look at the Church Lab at Harvard. George Church’s lab has spun out over 50 companies, including 16 in a single year (Harvard Website).
How?
They document obsessively
They focus on platform technologies
They normalize entrepreneurship from Day 1
They train people not just to do science—but to scale it
They work on inter-disciplinary research— more collaborations means better tranlation
The lab is structured to make translation possible. That’s not accidental. It’s designed.
And your lab can do the same.
SO, how can you upgrade?
You don’t need venture capital to run a great lab (or maybe you do? given the current funding scene). But you definitely need systems.
Start with one step:
Document a protocol
Build a 1-week onboarding checklist
Assign a mentor to a new lab member
Run a reproducibility review on your next manuscript
The problem with us scientists is that we find it hard to adopt new systems, even though we think up new ideas every day.
Our current system suffers from a lack of structure.
The next generation of impactful labs won’t just publish great papers (IMO, that should be a goal, which is a whole different topic to tackle). They’ll run like high-trust, high-performance teams that attract talent, partnerships, and funding.
Let’s think about efficient operations as foundation for lasting scientific impact.
What do you think? Do you have other ideas, opinions? Does your lab operate this way?
Let me know…