Science-Driven Business Models

Scientists studying a complex phenomenon don't start with experiments or even hypotheses. They first build a model. They use this model to run simulations and predict what they think will happen. They then run experiments to test the predictive accuracy of their model. If they get a different result than expected, i.e., the experiment invalidates their model, they update it and try again. This is the essence of the scientific method, which can readily be adapted into an equivalent entrepreneurial method: Model - Prioritize - Test 1. When faced with a new idea, we start with a business model describing how we intend to create, deliver, and capture customer value. 2. We then prioritize the riskiest assumptions in the model and make some predictions, which 3. We then attempt to validate through small and fast experiments. Like scientists, we attempt to learn why our predictions fail. Then use those insights to update our model and try again. Model-Prioritize-Test is how you navigate uncertainty in the new world. The Model-Prioritize-Test flywheel powers #ContinuousInnovation.

Tough pill to swallow as scientist at an early-stage biotech startup: If you don’t make the science work, on time & on budget, the company will die. Here’s a tried & tested framework for dealing with that 👇 Science at early-stage startups is 🧪 Fast-moving 🧪 Ever-changing 🧪 And first and foremost……outcomes-oriented! If you’ve spent your entire career to date in academia, this may feel unsettling at first. Here’s a framework for navigating outcomes-oriented science: 1️⃣ Zoom out. Get clear on the scientific & business outcome the startup needs to get to profitability. Focus on identifying unnecessary assumptions are constraining you - even if it’s an assumption your manager or CEO made! Example: You need a cell-line with particular characteristics to produce antibodies, which you will sell. Assumptions: 🧪We should make this cell line in house (should we make an off-the-shelf purchase instead?) 🧪The antibodies should be produced via cell line (is another system possible?) 2️⃣ Break the problem into its scientific/business parts. Example: What needs to be true about this cell line? It needs to grow quickly, cheaply, scale in some way, and have an optimized ability to produce antibodies. First principles thinking is key here! Biologists can take a lot from the engineering playbook. 3️⃣ Parallelize a few strategies to achieve this outcome. Consider: How can you ensure these strategies fundamentally de-risk each other? How can you try to solve the problem from multiple angles such at least one might yield the necessary outcome on time? Example strategies to parallelize: 🧪Purchase several cell lines which produce antibodies well. 🧪Chose 3 x potential in-house cell lines which are derived from very different sources. Optimize for reduced costs, quicker doubling times, and scale. 🧪Throw a small amount of resources at a long-shot technique which uses a microbial system to produce antibodies 4️⃣ Monitor progress regularly and cull projects as needed. Example: After 1 month, the microbial system is yielding surprisingly good results. 24 hours later, all cell line work is de-prioritized and the system starts again, zoomed in on microbials _________ Personally, I think that the startup model of science is exhilarating - it gets pretty addicting to see how much tangible impact you can make in a matter of months, rather than years!

Why We Chose Science Over Marketing. When you're burning £100K monthly on development, choosing science over marketing seems insane. But here's why we did it: 1. The Hard Truth ↳ 97% of supplements are marketing first ↳ 18 months to perfect one formula ↳ £2M invested in research ↳ Multiple failed iterations 2. The Real Cost ↳ Slower growth ↳ Higher development costs ↳ Lost "quick wins" ↳ Turned down influencer deals 3. The Heights Way ↳ Oxford scientists, not marketers ↳ Blood tests, not buzzwords ↳ Sustainable sourcing ↳ Evidence-based claims 4. What We Learned ↳ Trust takes longer to build ↳ Quality attracts quality ↳ Science sells itself ↳ Integrity compounds 5. The Results ↳ 250K newsletter subscribers ↳ 92% customer retention ↳ Zero medical claims disputed ↳ Community-driven growth Remember: • Marketing drives quarters • Science builds decades • Trust beats trends The real question: Are you building for quick wins or lasting impact? Share your experience with choosing the harder path 👇 ------------------------------------------------- Follow me Dan Murray-Serter 🧠 for more on habits and leadership. ♻️ Repost this if you think it can help someone in your network! 🖐️ P.S Join my newsletter The Science Of Success where I break down stories and studies of success to teach you how to turn it from probability to predictability here: https://lnkd.in/ecuRJtrr

Successful startup founders think like scientists. As an entrepreneur, relying on intuition and gut feelings can be tempting. But if you want to increase your chances of success, you might need to think like a scientist. I recently read a Harvard Business Review article titled "Why Entrepreneurs Should Think Like Scientists." The article highlights a study showing that startups using the scientific method generated significantly more revenue and were more likely to pivot away from unviable ideas. For the top 5%, this meant earning an additional €492,000 compared to those who didn’t apply this approach. So, how can you integrate the scientific method into your startup? 1️⃣ Test your assumptions Don’t just assume your idea will work. Test it with real customers and gather feedback. At Google for Startups, we create small pilot programs to avoid costly mistakes by learning early what works and what doesn’t. 2️⃣ Be ready to pivot Flexibility is key. If something isn’t working, be prepared to change direction. I’ve experienced this firsthand—by pivoting based on user feedback, we’ve turned potential failures into successes. 3️⃣ Use the scientific method Follow a structured process of observation, hypothesis, experimentation, and analysis. This methodical approach helps make informed decisions and drive continuous improvement. For practical application: 👉 Create an MVP Develop a basic version of your product to test your assumptions with real users. 👉 Run A/B tests Compare different versions of a feature to determine what performs best. 👉 Track your results Monitor your metrics to understand what’s working and what needs adjustment. The bottom line? Experimentation isn’t just a safety net; it’s a path to discovering what truly works for your startup. Whether you’re just starting out or looking to refine your approach, integrating the scientific method can be transformative to your startup. What’s your experience with using the scientific method in business?

For centuries, scientific progress was driven by observation. Early astronomers charted the sky, physicians recorded anatomy, and natural philosophers catalogued the world. Then, in the 1600s came a pivotal transformation, an awakening of deep curiosity in a period referred to as the Enlightenment. During this time observation evolved into hypothesis, experimentation, and prediction. Newton’s laws did not only describe falling apples; they enabled humanity to understand and even predict the forces at play. Science shifted from observing the natural world to theory and hypotheses of it, and through that change many of the modern conveniences we enjoy today were born. Business is undergoing a similar evolution. Operational excellence and performance analysis began with observation, measuring outputs, identifying inefficiencies, and standardising processes. Frameworks such as Lean and Six Sigma remain grounded in empirical observation and correlation. They excel at explaining what happens and, to a degree, why. Yet much of this remains retrospective. We monitor, we record, and we improve incrementally. In scientific terms, many organisations remain at the stage of saying, “If I drop this apple, it will fall.” Business cases, budgets, and cash flow forecasts are all forms of modelling. However, they extrapolate from established patterns and are based on the assumption that tomorrow will behave much like today. Digital twins and advanced simulations represent this progression. A digital twin replicates a real-world process or system, ingesting data and enabling changes to be tested virtually. These models are increasingly powered by artificial intelligence, including inference models that learn from vast datasets and forecast complex outcomes with growing accuracy. Looking ahead, the potential of quantum computing promises to accelerate this capability further, making it possible to simulate scenarios of previously unmanageable scale and complexity. As in science experiments, these tools could reveal how a change might ripple through a network before any adjustment is made in reality. Today, when we combine data with predictive analytics and simulation it allows organisations to shift from reactive observation to proactive change. Continuous improvement becomes continuous simulation. Rather than waiting for failure to surface opportunity, leaders can test “what if” scenarios in real time. Just as scientific theory enabled experimentation without incurring the full costs of trial and error, predictive modelling allows decision-makers to explore options, optimise outcomes, and allocate resources more effectively before committing to action. Science advanced when people began to theorise and not merely observe. Business now stands at a similar inflection point. Those who embrace predictive experimentation will not only understand their operations more deeply but, like Newton, begin to shape the very principles that define their success.

There are a lot of people in the digital and product world for whom 'science' = A/B Testing, and absolutely nothing else qualifies. But this is not how real science works. Imagine if the only way anything was called 'science' was if it had a statistically controlled split test behind it. We would never have progressed very far. Cosmology is a great analogy. Cosmologists observe the universe and then build mathematical models to try and explain the patterns they see. Further observations then provide evidence in support of or against the model, which can then be refined to try and better fit the observations. There is an established methodology and system behind the way knowledge advances. In modern times, some controlled experiments might be run on very specific aspects of physics to help these observations, but the field was advanced for centuries without these. Our customers and markets are very much like the cosmos; you cannot run controlled experiments on the totality of the world you are trying to affect with your business. But it is possible to emulate this scientific approach. You can observe customer behaviour and market conditions in a myriad different ways. You can develop models to try and explain customer behaviour and how the decisions you make affect these external conditions. You can make changes and observe whether your mental models might be correct. You can run controlled experiments on very specific aspects to help advance your models. You can revise your models. Most importantly, you can have careful methodologies and systems to do all of this. But the problem is that these methodologies, processes and systems simply do not exist. We have endless amounts of data and teams of very enthusiastic people, but is anyone really working to anything like this method? The 'models' we can create are not mathematical but are possible through 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 and theory. The methodology is possible through 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 and workflow management. The theories and their revision are possible through 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗻𝗱 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 about business strategy and innovation. It is entirely possible to be scientific without A/B testing, although including that will, of course, go an incredibly long way. A/B testing is very powerful, but it is mostly completely divorced from anything like this wider methodology and process. #experimentation #cro #productmanagement #growth #digitalexperience #experimentationledgrowth #elg  

The “biotech / techbio / synbio / genbio” labels are convenient shorthand, but they break down fast in funding conversations. Most confusion comes from mixing up two different questions: 1. Where biology is being applied 2. How the work is being done A clean framework matters because it lets  ➡️ Scientists describe the work clearly ➡️ Investors and capital allocators pattern match risk and return: timelines, regulatory exposure, technical vs scale risk, and whether value comes from an asset, a platform, a tool, or a service. 1. The umbrella: Bioeconomy “Life sciences” is often read as “human health.”   Bioeconomy is the better umbrella for biology applied across industries, such as health, food & ag, consumer, industrial, environment, and defense. That’s the “where.” 2. The approach: how the work is done Across any of those sectors, these labels describe the build approach: ▫️ Biotech: applying biology to create products and processes (broad category)   ▫️ SynBio / EngBio: applying engineering principles to biology (design-build-test-learn) ▫️ TechBio: accelerating biology with software, automation, and data (the differentiator is the engine)   ▫️ GenBio: using generative models to design and optimize biological sequences and structures (DNA/RNA, proteins, pathways) This also includes the enabling stack: sequencing (read), DNA synthesis (write), automation (run), and AI agents (decide). 3. The business model: how value is captured This is the missing layer in most label debates. Two companies can operate in the same sector and use similar methods, but be fundamentally different businesses. ▫️ Asset-led: value is driven by a specific product   ▫️ Platform-led: value is driven by a reusable capability that repeatedly produces assets   ▫️ Tools (picks-and-shovels): value is driven by enabling others (software, hardware, biological tools)   ▫️ Service-led: value is driven by doing the work for others (CRO/CDMO-style) ➡️ A practical way to answer “what are you building?”: Sector (where) + approach (how) + business model (how value is captured). Which term gets used a lot in your world, but often means different things to different people? Synbio, techbio, "platform"?

Ever wondered how startups using #ArtificialIntelligence differ from traditional IT startups in their business models? This paper delves into the unique business models of #AI startups, comparing them to conventional IT-related business models. It identifies four archetypal business model patterns for AI startups: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. The paper also discusses three distinctive aspects that set AI startup business models apart: new value propositions enabled by AI, different roles of data in value creation, and the impact of AI technology on the overall business logic. 1️⃣ AI startups often introduce new value propositions that are not possible without AI capabilities. For example, they can offer highly personalized services or automate complex tasks. 2️⃣ Data plays a different role in AI startups. Unlike traditional IT startups where data might be a by-product, in AI startups, data is often central to the value creation process. 3️⃣ The overall business logic of AI startups is influenced by the AI technology itself. This means the way they operate, interact with customers, and even their revenue models can be fundamentally different from traditional IT startups. Reading this paper will give you a deeper understanding of the unique characteristics and opportunities presented by AI startups. It provides valuable insights for entrepreneurs, investors, and anyone interested in the evolving landscape of AI and startups. ✍🏻 Weber, M., Beutter, M., Weking, J. et al. AI Startup Business Models. Bus Inf Syst Eng 64, 91–109 (2022). DOI: 10.1007/s12599-021-00732-w ✅ Sign up for our newsletter to stay updated on the most fascinating studies related to digital health and innovation: https://lnkd.in/eR7qichj

I’m always interested in case studies of companies that try to fight biopharma’s R&D productivity challenge by fundamentally doing things differently. I believe that that BridgeBio’s case is one such example. Not because it has found a magic scientific shortcut, but because it is redesigning how biotech risk is structured, financed, and managed. Its scientific focus is paired with something just as important - a financial architecture that actually fits the risk profile of drug development. By diversifying programs, BridgeBio reduced perceived binary risk and gained access to lower-cost, long-dated capital. The result? More programs can move forward, capital lasts longer and decisions become more disciplined. And importantly, failures don’t threaten the entire company. Best thing is that this isn’t just an innovation for the sake of novelty, it’s innovation because the old model often struggles under today’s complexity. And as they clearly demonstrate, there are some interesting results they have to show for it. The industry doesn’t only need better science. It also needs business models that match the reality of scientific uncertainty. And if it keeps running 10–15 year, high-risk, high-cost - inefficient R&D cycles with short-term capital structures and binary company setups… We shouldn’t be surprised when productivity keeps falling. Source: Chinmay Shukla, Irwin Tendler, Neil Kumar, Andrew W. Lo, Genetic Targets, Financial Creativity: BridgeBio’s Model for Sustainable Drug Development, Drug Discovery Today, Volume 31, Issue 1, 2026, 104583, ISSN 1359-6446. ----- Hi, I’m Martin, and I’m passionate about learning and exploring how we can improve value creation across biopharma. If you enjoy posts like these, or want to discuss how to drive more value in the industry - let’s connect!

The graveyard of industrial biomanufacturing is crowded. But it’s only a horror story if you ignore the clues. Treated properly, that graveyard becomes a playbook. In Issue #125 of Better Bioeconomy, I spoke with Veronica Breckenridge, Founder and Managing Partner at First Bight Ventures, about their recent 'Industrial Biomanufacturing Graveyard' report and how she uses those failures to back companies that actually scale, survive, and win. Veronica also has one of the most interesting operating backgrounds I’ve seen in this space: Motorola → Apple → Tesla → Industrial biomanufacturing investor. Five insights that stood out: 1. Most failures were commercial and operational, not scientific: The graveyard data is clear: lab and pilot usually worked. Companies died when they hit the plant and could not make product at a cost, volume, or reliability the market would accept. The core problems were price corridors, competition, and execution, not biology itself. 2. Industrial bio investing favours concentrated portfolios over spray-and-pray: This isn’t software where a single 100x outlier fixes the portfolio. Survival and exit density matter. A concentrated portfolio gives investors the bandwidth for real hands-on support across manufacturability, commercial traction, and FOAK execution. 3. The founder profile that scales science and operations: Veronica screens for three things: coachability, a strong business bias (unit economics, customers, pricing), and the ability to grow with the company as it moves from lab to plant. Those traits often matter more than whether the science is the most elegant on paper. 4. Start in higher-margin, drop-in niches, not hero-commodity markets: Many first-wave companies went straight after low-margin commodity molecules to tell a big TAM story, then crashed into petro incumbents on cost. The smarter path is to start in speciality applications where biology has a functional edge, volumes are manageable, and the product can run through existing equipment. 5. Equity capital efficiency beats the fantasy of CAPEX-light: First-of-a-kind facilities are real hardware: tanks, utilities, downstream equipment. Equity should take early technical and commercial risk, but the bulk of the steel and concrete needs to be carried by equipment finance, incentives, and strategic partners. The winners design capital stacks that match physical reality instead of pitching biomanufacturing as asset-light. Read full article: https://lnkd.in/gbJMUgxW