Food is just data with a shelf life

From Crystal-Ball Trends to Biometrically Tuned Snacks

For decades, the food industry has launched “one-size-fits-all” products and relied on outdated, slow surveys to figure out what people want. That’s changing.

AI, wearables, and neuroscience are shifting how food is created, marketed, and personalised. We’re entering an era where snacks don’t just taste good—they’re built around your biology, your mood, and your unique sensory profile.

If you're working anywhere along the food value chain, this shift is impossible to ignore. A new generation of AI tools is replacing "launch and hope" with smarter, faster, and more personalised development—long before products hit the shelves.

Let’s break down the three most important AI capabilities transforming food-tech today, and where they fit in the value chain:

1. Trend Prediction

Where it fits: Early-stage insights and R&D
Think of it as a crystal ball for emerging tastes and food formats.

What it does:
AI scans billions of data points—TikTok trends, grocery searches, menus, and even recipe photos—to spot patterns before they go mainstream. It doesn't just track what’s trending now, but what will trend six months from now.

It also identifies whitespace opportunities: where flavor combinations or ingredient claims are missing in the current market. For example, discovering that sour yuzu is trending among Gen Z but is still absent in most energy drinks. Or that gut-health claims are exploding in snack bars—but no one has applied them to savory formats yet.

Why it matters:

• Gives R&D teams weekly insight reports, not just annual summaries

• Reduces guesswork and blind spend on concepts that won’t resonate

• Helps marketers and sales teams craft data-backed narratives

• Allows faster packaging and branding decisions (e.g., bold fonts and citrus visuals for summer sour trends)

• Gives commercial teams credible storytelling tools: “We’re launching this now because our AI shows it’s peaking in Q3”

The edge
This data becomes a proprietary trend graph over time. The more a company feeds in category-specific data, the smarter and more defensible their model gets.

2. Brain & Body Tuning

Where it fits: Product formulation and pilot testing. This is where food starts functioning more like a supplement—or even a wearable.

What it does:
AI analyzes how your body responds to specific recipes using biometric signals—EEG, HRV, glucose monitors, gut microbiome tests, and more. It helps answer questions like:

• Which ingredients help with focus?

• What combination leads to a more stable blood sugar response?

• Can this drink measurably reduce stress?

Rather than relying on consumer claims like “I felt more focused,” the AI quantifies whether a bite or sip produced a measurable effect—like improved mental clarity, lowered cortisol, or a flatter glucose curve.These signals then guide new iterations of the recipe, optimized for physiological effect—not just taste.

Why it matters:

• Enables specific, proof-based claims like “clinically shown to sustain focus for 90 minutes”

• Creates high-value, proprietary datasets (ingredient → biometric outcome) that are hard for competitors to reverse-engineer

• Shortens iteration cycles: instead of launching, waiting, and adjusting, you optimize in the lab

• Allows dose precision: e.g., 1.8g of lion’s mane works better than 2.5g for sustained attention, based on continuous data

• Expands the use case horizon—products aren’t just tasty, they’re tools for performance, calm, recovery, or hormonal support

• Justifies premium pricing for bio-validated foods
  

Strategic value
These models can replace parts of clinical testing, simulate outcomes before formulation, and create IP-protected “bio-labels” that set products apart on the shelf.

3. Taste Profiling

Where it fits: Personalisation and post-launch targeting

Taste is subjective—AI is making it objective.

What it does:

Everyone experiences flavor differently. Taste profiling uses games, micro-surveys, purchase history, and (optional) genetic markers to build a sensory “fingerprint” for each consumer. From that, AI can recommend products with stunning accuracy: “This wine matches your aroma preferences and bitterness tolerance with 92% confidence.”

Over time, as users rate or purchase, their taste vector sharpens. It even enables real-time nudging: “You’re low-energy—this citrus-ginger drink fits your profile and supports focus.”.

Over time, feedback loops back into R&D, sharpening future product design.

Why it matters:

• Boosts retention and converts new customers into long-term subscribers: products feel custom-built, not generic

• Slashes sampling and return costs—especially in wine, supplements, or premium snacks

• Provides brands with a unique dataset (taste → behavior) they own and can’t buy off the shelf

• Enables micro-targeting by geography: e.g., Region A loves spice-forward kombucha; Region B prefers floral and sweet

Beyond DTC
Retailers and CPG brands can use this data to optimize store assortments, improve placement, or even dynamically personalize promotions at the shelf or online.

How It All Comes Together: The Virtuous Loop

These three technologies don’t live in isolation. When combined, they create a powerful, compounding loop:

1. Predict (Trend Engine) : NLP and computer vision models detect new trends—e.g., a spike in yuzu + adaptogens on Gen Z’s social feeds.

2. Formulate/ Validate: Prototypes are tested using wearables or biometric tools. AI helps identify which version truly delivers the promised benefit—like a better focus window. Trend engine now sees which trend ideas actually work physiologically, rank-ordering future bets by both buzz and biological efficacy.

3. Personalise: The final product is matched to individual taste profiles, improving satisfaction and retention. Adds live market-fit data: which formulas resonate with which sensory clusters—fuel for more precise trend segmentation

4. Capture & Loop Back: Real-world feedback—purchase behavior, reviews, biometric responses—is fed back into the system, improving the next cycle. The trend engine re-trains on outcome-weighted data, making its next forecasts not only fashionable but also functionally proven and sensorially verified.

Each turn of the loop sharpens the next, reducing risk and time-to-market while increasing up consumer satisfaction.

For anyone building—or betting on—the next wave of food innovation, mastering these three layers is no longer optional. It’s the playbook for moving from “I hope this sells” to “I already know who will love this and why.”

Why the Loop Compounds Over Time

1. Data Network Effects
   Every consumer interaction adds new labelled datapoints, making subsequent predictions sharper. A rival starting tomorrow can’t simply “match the model”—they lack the historical bio-sensory graph.

2. Shrinking R&D Cycles
   Classic CPG cycles run 18–24 months. Firms running this loop see early customers tasting pilot batches within 6–12 weeks, because trend filtering and bio-validation happen almost in parallel.

3. Capital Efficiency
   Each “spin” reallocates spend from low-yield shelf tests to high-signal experiments. Dollar for dollar, the model gets smarter, while traditional players keep burning on failed line extensions.

4. Regulatory & IP Moat
   Biometrically backed claims (“reduces post-meal glucose by 22 %”) require proprietary datasets plus statistical models that stand up to regulators. Once filings are in, competitors must build their own loop—or license yours.

A Few Reality Checks

• Bias: Early adopters may skew toward affluent, health-tracking users. Continuous, diverse sampling helps keep the model fair.

• Org Design: This AI loop isn’t a plug-and-play tool. Teams from R&D, insights, marketing and product need to work from a shared data backbone to fully unlock its value.

• Accuracy and Interpretation of Biometric Data: Biometric data from consumer-grade wearables can have variability and may not always be as accurate as clinical-grade measurements. Moreover, interpreting this data correctly and translating it into actionable, safe, and effective nutritional advice is complex.

• Regulatory Hurdles & Claim Substantiation: Making specific health claims (e.g., "improves focus") using AI and biometric data faces strict regulatory scrutiny. Substantiating these claims requires robust, scientifically valid data, a potentially lengthy and costly process. The blurring line between "food" and "medical product" adds further regulatory complexity.

• Scalability of True Personalization: While AI excels at creating personalized profiles, translating these into truly individualized product formulations and mass-market delivery presents significant operational and supply chain challenges. The vision of unique products for everyone may conflict with mass production realities.

• Privacy: Handling biometric and sensory data comes with responsibility. Consent and GDPR-grade data security are essential.

The food industry is at a moment, where AI and biometric insights are transforming product development from an art into a science. By predicting trends, validating effects, and personalizing taste, companies can build a future where food is smarter, more effective, and deeply connected to individual needs.

---

Thanks for reading. Reach me with tips, questions, and feedback on Twitter or LinkedIn.

If you enjoy this newsletter, why not share it with your friends and co-workers?

---

The views expressed here are my own. All information contained in here has been obtained from publicly available information. While taken from sources believed to be reliable I have not independently verified such information and make no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. For questions simply reply to this email.

Driving Insights in the Age of AI

When Knowing Isn’t Enough. Overcoming the Commoditization of Knowledge

The Knowledge Saturation Paradox

Remember when the hardest part of any project was finding the information? Today the problem has flipped: every answer sits one prompt away and the real work is deciding which answers matter. Knowledge has become a commodity—abundant, low‑cost, and almost weightless. Yet with this abundance comes a paradox: when everyone knows everything, knowledge itself loses strategic value. If access is universal, where does the competitive edge come from?

The Algorithmic Monoculture (The Quiet Shift No‑One Talks About)

Large language models promised infinite originality, yet most of them were trained on the same public soup of text. Ask three different chatbots a nuanced question and you’ll often get the same middle‑of‑the‑road reply, polished but predictable. This quiet convergence—call it an algorithmic monoculture—flattens thinking into consensus mush. Competitive edge now depends on escaping that gravitational pull toward safe, identical conclusions.

From Information Advantage to Insight Advantage

Before the internet, having the facts first was enough. In the Google era, speed mattered. In the LLM era, neither is special: everyone has the facts instantly. What endures is the ability to turn raw material into a perspective that others haven’t considered yet. Insight is interpretation plus intent which is the moment data crosses over into a clear, confident "therefore we should…".

How AI Accelerates Commoditization

AI is not just leveling the playing field – it’s flattening it. Drafting a memo, profiling a market, sifting annual reports—jobs that once signaled expertise now look like table stakes. As default knowledge rises, decision makers risk drowning in a warm bath of competent but interchangeable analysis. Noise increases, signal stalls.

The Hidden Cost of Cognitive Off‑Loading

There’s a subtler danger in letting machines chew the thinking for us. The less mental friction we feel, the weaker our pattern‑recognition muscles become. Over time companies can lose their intuitive sense for an industry—the half‑remembered anecdotes and intuitive leaps that never make it into a database but often spark the breakthrough. Insight, therefore, is not just about speed – it’s also about retention: keeping enough mental friction to foster novel connections humans alone can see.

The Gap: Insights as the New Scarcity

That is why genuine insight is suddenly scarce again. It requires synthesis across disciplines, an eye for context, and crucially the confidence to lean into a reading that might be wrong. Good insights feel slightly uncomfortable; they carry tension. They are also, for a brief window, non‑fungible: once an insight is obvious to everyone, its value collapses. In an age where everyone can access the same knowledge base, those who can consistently extract real insights will create disproportionate value.

Expert Insight: The Last Offline Frontier

Talk to people who spend their days on factory floors, in grow‑rooms, or inside regulatory offices and you’ll hear things you won’t find in any dataset. A maintenance lead knows the seal that always fails in July; a winemaker can taste next year’s mold risk; a junior civil servant might mention a clause that’s just been struck from a draft bill. None of this tacit knowledge is searchable, and no model can predict it before someone says it out loud. Surfacing it means picking up the phone, scheduling coffees, and walking the shop floor—the slow, slightly awkward work most teams skip. Precisely because it is messy and offline, it remains the last well of insight that hasn’t been flattened into commodity information. The insight itself may resist commoditisation, but with the right tools its discovery and access can be shared far more widely.

Winning Insight Velocity and Insight Arbitrage

Having a clever perspective is good; having it first is better. Speed matters – but in the future, it’s the speed of generating and accessing insights that will define leaders.

We measure that gap—raw information to action taken—as insight velocity. Move quickly and you enjoy a period of insight arbitrage, exploiting a reading before the market digests it. Our job is to shorten that loop without losing the human judgment that keeps it sharp.

The Quiet Rise of Insight Privacy (The New Trade Secret)

Firms used to guard datasets. Now the smarter ones guard interpretive frameworks: proprietary taxonomies, contrarian heuristics, shadow models that never leave the vault. We expect this to merge into a new discipline—insight privacy—where the moat is not what you know (raw data) but how you see (the lens through which the data is viewed). Protecting that lens from replication becomes paramount.

Conclusion

Knowledge now costs nothing while insight can still tilt a market, converting one into the other has become both the hardest and the most profitable job in business. The challenge—and the opportunity—is to deliver those insights at machine speed to the people bold enough to act on them.

---

Thanks for reading. Reach me with tips, questions, and feedback on Twitter or LinkedIn.

If you enjoy this newsletter, why not share it with your friends and co-workers?

---

The views expressed here are my own. All information contained in here has been obtained from publicly available information. While taken from sources believed to be reliable I have not independently verified such information and make no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. For questions simply reply to this email.

AI Agents in VC: How to Automate the Investment Process, Starting with Data Rooms

Manually reviewing data rooms is outdated—AI agents do it better and faster

I will be kicking off a new series where I write about how to build AI agents to support investment processes. I will build these AI agents in parallel and improve them as we go along. This is a first take.

---

No big talk about how AI will change the VC industry and how LPs will be demanding AI strategies in the future. Let’s skip that and dive straight into a concrete use case: the data room review. Today, these large collections of documents are still manually reviewed, page by page.

The usual workflow: Get access, check for completeness, analyze all sorts of documents, discuss internally, and send endless Q&A rounds to the startup. This takes days and involves the entire deal team. But what if you could leverage AI agents to do this in hours if not minutes – with the same accuracy and speed?

This first post will walk you through building an AI agent step by step and mainly focusing on the principles that apply. Just like we moved from manual data entry to automated CRMs, AI agents are the next gold standard for VC funds. You already know that – that’s why you’re here. Let’s dive in. Simple explanations, no coding or technical background needed.

What are the key challenges in building an AI agent for data room reviews?

The challenge an AI agent must solve when reviewing a data room is handling large volumes of unstructured data efficiently. Data rooms often contain hundreds of documents in various formats – PDFs, Word files, Excel sheets, screenshots and presentations – each with different structures, languages, and complexities. The AI agent needs to quickly access, parse, and understand this data, identifying key information such as financial metrics, legal clauses and market data. It must also cross-reference documents, highlight inconsistencies, and flag missing information. Most importantly, the AI agent must provide clear, accurate summaries and insights that the VC team can trust, all while maintaining data security and adapting to different data room structures from deal to deal.

The tech stack should correspond to these challenges. The following principles should apply.

1. Accuracy: Extract and summarize information with minimal errors, ensuring reliable insights.

2. Speed: Process large document sets quickly to save time without compromising quality.

3. Context Awareness: Understand the VC’s needs, focusing on relevant details like financials, legal risks, and market data.

4. Transparency: Reference the exact source documents for each insight, allowing the deal team to verify critical points easily.

The following way we can resolve these principles is by including a simple tech stack.

• Accuracy: Integrate the latest model to let your AI agent use cutting-edge models for precise data extraction and summarization.

• Speed: Use parallel processing capabilities to review multiple documents at once, drastically reducing the time needed for data room reviews.

• Context Awareness & Summarization: Implement a vector database to store document embeddings, allowing the AI agent to understand and maintain context across various documents and data points. AI agents can generate concise summaries of lengthy documents.

• Transparency: Store document metadata in the vector database, enabling the AI agent to reference original documents for every insight it provides, ensuring easy verification and trust in the results.

Let’s build

We will build two AI agents. One will handle uploading hundreds of files to the vector database, while the other will answer questions based on the stored data. This architecture is flexible and can be adjusted to fit your needs.

Architecture

1. Document storage: Google Drive: Example data: Good Food Institute reports about alternative proteins. (This is still a blog about the Bioeconomy ;-) )

2. Automation and workflow management: n8n.io

3. Vector database for semantic search and retrieval: Pinecone. This is a specialized database that stores data as numerical vectors. It’s useful for “semantic search,” where the database finds information based on meaning, not just keywords.

4. AI Agent: OpenAI - model o4. Note that you can use any type of AI model here.

The first agent is structured as follows:

It’s a visual workflow showing how files from Google Drive are fetched, processed, and then stored as embeddings in Pinecone. Here’s the step‐by‐step flow as depicted:

1. Trigger: The flow starts when the user clicks “workflow.”

2. Google Drive (Search): The first Google Drive step is set up to locate files or folders in Drive (e.g., searching a directory).

3. Google Drive (Download): Once found, the files are passed to a second Google Drive step that downloads them.

4. Loop Over Items: Each downloaded file is fed into a loop, which processes the files one by one.

5. Default Data Loader + Recursive Text Splitter: Within the loop, a data loader reads each file, and a text splitter breaks the text into manageable chunks.

6. OpenAI Embeddings: Those text chunks are then sent to an OpenAI Embeddings step to generate vector embeddings.

7. Pinecone Vector Store: Finally, the embeddings are stored in Pinecone (a vector database) so they can be searched or queried later.

This setup helps populate your vector database and prepares you for data room reviews. Below is an example data room, which I populated with Good Food Institute reports that vary in size and content.

The vector database with the transformed files:

Now the second part of the AI agent is a RAG setup (Retrieval-Augmented Generation) also built with n8n. It integrates OpenAI’s Chat Model, Pinecone for vector storage, and a memory buffer to combine AI responses with external knowledge (in our case its the data room). Essentially, the AI system retrieves relevant documents from a database to generate answers that are more accurate and grounded in real data. This means it doesn’t rely on pre-trained data or web searches—it only generates answers based on the specific documents and data provided. This is exactly what we need when reviewing data rooms for target companies.

The second agent is structured as follows:

1. Trigger: "When chat message received": This initiates the workflow when a new message is received.

2. AI Agent (Tools Agent): The core processing unit that determines how to handle incoming queries. It integrates memory, chat models, and vector-based retrieval.

   1. Memory: Window Buffer Memory: Stores the conversation history to provide context for better responses.

   2. Chat Models: OpenAI Chat Model & OpenAI Chat Model1: These provide responses based on the chat history and retrieved information.

3. Vector Store: Pinecone Vector Store: Stores the embeddings generated with the first AI agent for efficient similarity searches.

4. Embeddings: OpenAI Embeddings: Converts text into vector embeddings for retrieval.

5. Answering Questions via a Vector Store: This module retrieves relevant information from Pinecone and combines it with the AI model’s response.

How the System Works Here:

1. A chat message is received.

2. The AI Agent determines if it can answer using memory or if it needs external knowledge.

3. If external knowledge is required:

   • The message is converted into an embedding using OpenAI Embeddings.

   • The system queries Pinecone (our data room) to find similar stored information.

   • The retrieved information is fed to the OpenAI Chat Model to generate a final response.

4. The response is sent back to the user.

This setup allows the AI agent to combine live AI responses with stored knowledge for better, more context-aware answers. It is ideal for handling queries where fast real-time knowledge retrieval is necessary (e.g., data room review, document-based Q&A, and investment research).

And that’s it—just like that. Simple magic. Now, let’s return to our data room and demonstrate how effectively it can answer our questions.

Three Examples

General questions:

Prompt: I want to learn about plant based eggs. Summarize what you can find in the dataroom.

Specific questions:

Prompt: Tell me something about mung beans based on the protein primer.

Original Source:

Outcome:

Document review and Q&A:

Prompt: Please review the document: Cell line development and utilisation trends in the cultivated meat industry. I would like to understand what are open questions that I could ask an expert that are not yet covered by the document.

---

Wrapping up, this is a first take, but there’s more to improve. Some ideas to play around with:

• Expand access. Don’t limit the Agent to a single data room. Provide access to all your internal documents and reports. This ensures a deeper knowledge base and more accurate insights.

• Compare notes. Include your personal notes. Let the Agent cross-check them with findings from previous data rooms. This can reveal patterns and highlight any inconsistencies.

• Use standardized templates. Create a structured Q&A format. Send this template directly to the startup for a quick, consistent overview. It saves time and keeps everything organized.

• Add a data room system. Make it easy to upload and update documents. Keep everything in one accessible, secure location.

• Add memory. Enable the system to store key information and data room findings (e.g. market size data) in a memory database, making it easily accessible without the need for manual note-taking.

Follow along as we explore how to leverage AI agents for your fund, integrate AI with existing VC tools, automate investment processes, and more. If you want to build this for your fund, reach out—I’m happy to help set it up and answer any questions, especially on how to integrate it seamlessly into your workflows.

---

Thanks for reading. Reach me with tips, questions, and feedback on Twitter or LinkedIn.

If you enjoy this newsletter, why not share it with your friends and co-workers?

---

The views expressed here are my own. All information contained in here has been obtained from publicly available information. While taken from sources believed to be reliable I have not independently verified such information and make no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. For questions simply reply to this email.

Bioeconomy Startups: A Dive into De-risking Strategies

Extracting Lessons from Established Startups

Like every other industry, the bioeconomy is facing its own challenges. However, as in any other sector, these challenges are being tackled in a manner specific to the bioeconomy. Companies here have developed strategies to overcome them and minimise the resulting risks. So what are these strategies, what risks do they aim to minimise and how do they help ­companies to overcome their challenges?

Start-ups founded on the basis of bio-based materials play a key role in the emerging bioeconomy. Yet they face a number of challenges, particularly when it comes to scaling up technologies and processes. Unlike traditional chemical processes, biological processes such as fermentation are extremely heterogeneous and their complexity makes it difficult to easily model or predict all parameters.

Engineering scale-up de-risking

As a general rule, the key to successfully scaling up a bioeconomy start-up therefore lies in having a clear understanding not just of the product itself, but also of the process involved in creating that product, so that the uncertainties involved in ­scaling up that process can be minimised. This makes it possible to monitor certain parameters and to find a sweet spot. Processes in the bioeconomy in particular are often difficult to predict, especially with regard to the variability of feedstocks and organisms. The transition from pilot phase to demonstration phase and ultimately to commercial scaling is typical of start-ups in this field – and some of these stages cannot be skipped.

Collaboration with research institutions or Contract Manufacturing Organisations (CMOs) can play a crucial role in outsourcing certain processes and spread­ing the risk when scaling up ­engineering processes. Cooperation with established companies and their own ­research and development units is ­another possibility. Uncertainty can be minimised through joint ventures and clear guidelines for scaling. In practice, this means companies entering into joint cooperation and off-take agreements, which define the type, quantity and ­quality of the products to be produced in order to meet customer requirements. Consideration should also be given to ­fully outsourcing individual production steps to external parties. For example, many companies work with CMOs to ­outsource fermentation processes. Specialised companies are commissioned to replicate the process based on the parameters provided. Collaborating in this way allows start-ups to focus on their core strengths.

However, the availability of fermenta­tion capacities is proving a particular challenge at the moment. Yet in spite of these issues, partnerships like these are becoming more common. The Spanish company Heura, which operates in the ­alternative protein sector, is one example of an enterprise that uses CMOs for its production. The benefits of this approach are faster production and a business ­model with low capital expenditure (­capex). Such scale-up strategies are ­becoming increasingly mainstream and are helping to bring innovative bioeco­nomy products to the market quickly and successfully.

Feedstock de-risking

Another key issue specific to the bioeconomy and closely linked to the scaling of start-ups is the risks associated with ­finding and providing feedstocks. The question here is whether to opt for a ­centralised or decentralised provision model. In other words, whether to build a large factory or several smaller facilities and whether to use local or globally ­sourced feedstocks. If a customer demands large quantities of a consistent product, start-ups that rely on a variety of local feedstocks could soon reach their ­limits. In these cases, it makes sense to ­enter into joint ventures with established companies that have large facilities and can commit to providing consistent feedstock. There are numerous advantages to a steady-stream partnership like this. It ­attracts the interest of strategic investors and creates a sound basis for protecting the company against competitors.

Protix - Production Facility

When a bioeconomy start-up begins cooperating with a large company and, in doing so, gains access to their side streams, this creates a competitive advantage that is difficult to get past. One prominent example is Protix – a company ­specialising in insect breeding – which ­recently entered into a partnership with Tyson Foods, a world leader in beef, ­poultry and pork production. This partnership includes the establishment of a joint venture to build a facility for insect ingredients (ingredients from insects: proteins, lipids) in the USA. Alongside this collaboration, Tyson Foods is investing in Protix to accelerate the company’s global expansion ambitions. In addition, by-­products that Tyson Foods already generates are immediately put to use as feedstock for the insects.

Bioeconomy start-ups with specific product ideas can harness and build on these emerging partnerships, allowing them to move away from highly distri­buted side streams towards the strategic use of these resources as an essential component of large companies’ business strategies. A link to a larger company with a consistent, industrially standardised feedstock supply also guarantees the consistency of the end product, a parti­cularly important consideration in the bioeconomy.

Product-to-market de-risking

Another critical aspect of the bioeconomy is the risks associated with bringing products to market. The product must meet the requirements of the market, which may sound like stating the obvious, but in fact is not: start-up founders are often so convinced of their innovation that they fail to correctly assess the market ­demand. Product consistency is another factor that plays a decisive role in de-­risking a potential market fail. When a ­customer requests large quantities of a product, it is important to ensure that ­every unit of that product is consistent and meets quality standards. If the business model is only profitable for a limited production volume and difficulties arise during scaling, this can pose significant risks for market entry.

Another proven de-risking approach is to develop clear strategies for the market launch right from the outset. This includes identifying potential target markets, assessing market trends and analysing competitors. Alliances and partnerships can also help to facilitate a market entry, especially when it comes to accessing distribution channels and opening up new markets. The commercialisation of bioeconomy products also requires greater ­awareness of environmental and consumer concerns. The sustainability aspect of bio-based products needs to be communicated clearly and convincingly. This is particularly important as consumers (and therefore also producers) are increasingly interested in environmentally friendly and sustainable products.

One interesting example of this is Peel Pioneers, a company that converts ­orange peels – a waste stream – into fibers for food ingredients. The upcycling process and the sustainability aspect, in ­conjunction with precisely defined product specifications and a targeted ­approach aimed at specific sub-markets such as bakeries or plant-based meat, is an example of how risk minimisation ­mechanisms can be successfully implemented in product-to-market strategies.

Peel Pioneers - Orange Peel Collecting Unit

Financial de-risking

One crucial aspect in de-risking bioeco­nomy start-ups is, of course, the financial aspect, and it requires partners and investors who understand not only the concept of highly specialised bioeconomy engineering scale-ups, but also the capital-­intensive requirements that usually go hand in hand with them. However, the question of how to raise the required capital is the responsibility of the CFO, who ideally knows exactly where to obtain the necessary funds.

A key aspect of financial de-risking is the ratio of capital employed to capex ­efficiency, known as the capex-to-efficiency ratio. The aim is to ensure that investments in facilities and production technologies are efficient and deliver an appropriate return, with the business model playing a decisive role in this respect. The question of whether it is always necessary for a company to operate capital-efficient production itself or whether it is better to offer licences is a key de-risking mechanism. It is vital to analyse from the outset how capital-intensive production would be in the event of upscaling and to ­structure the financing on this basis. If the business model relies on continuously building new factories and repeatedly ­raising capital to do so, this could be problematic in the long term and reduce the likelihood of success – provided that cost degression is difficult to achieve or can only be achieved with very high production capacities.

Alongside the search for investors, it is also important to consider other forms of funding, such as the EU’s Horizon Europe programme, which can be combined with complementary programmes available at national level, including structural and ­rural development funds, the ESIF and the Recovery and Resilience Facility under the EU‘s NextGenerationEU Covid recovery instrument. Using these funding mechanisms also minimises the risk for investors. The EIC Accelerator programme ­under Horizon Europe, Pillar 3 – Innovative Europe – could be particularly appealing for introducing external investors to bioeconomy start-ups, as it aims to help innovative SMEs, including start-ups, to realise their ideas in all sectors, including the bioeconomy. Another example is the European Investment Bank , which recently granted a loan of EUR 40 million to the company In Ovo to end the practice of killing male chicks. The EIB and the ­European Commission have also launched the European Circular Bioeconomy Fund, the first venture capital fund dedicated ­exclusively to the (circular) bioeconomy, taking up the challenge of unlocking the economic potential of this sector.

In Ovo - Sorting Machine

**This article was initially published on Going Public and has been subsequently adjusted for presentation here. Any updates or amendments are not reflective of the initial content's endorsement or approval.

---

Thanks for reading. Reach me with tips, questions, and feedback on Twitter or LinkedIn.

If you enjoy this newsletter, why not share it with your friends and co-workers?

---

The views expressed here are my own. All information contained in here has been obtained from publicly available information. While taken from sources believed to be reliable I have not independently verified such information and make no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. For questions simply reply to this email.

🌳 Bioeconomy Newsletter #26

News, recent funding announcements and very interesting reads

Funding

🌿 BeZero Carbon - US - $ 50M - Series B - Voluntary carbon market scoring platform

🌿 BioFluff - US - $ 500k - Pre-Seed - Plant-based fur mimicking conventional animal fur and popular synthetics

🌿 Butter - US - $ 9M - Series A - Management system for food distribution businesses

🌿 Clarifruit - Israel - $ 12M - Series A - AI-powered quality control technology for fresh produce

🌿 Cultimate Foods - Germany - EUR 700k - Pre-Seed - Cultivated fat for hybrid alt-meat products

🌿 Eden Bio - UK - GBP 1M - Seed - Using machine learning to increase protein yield by optimizing microorganism strains

🌿 Freight Farms - US - $ 17.5M - Series B - Vertical hydroponic farms in shipping containers

🌿 Overstory - Netherlands - EUR 5.2M - Early VC - Real-time analysis of natural resources using high-resolution satellite imagery and AI

🌿 Peptyde Bio - US - $ 1.2M - Pre-Seed - Nature-based biofungicides

🌿 Soft Robotics - US - $ 26M - Series C - Automated picking solutions using soft robotic grippers, 3D machine perception, and artificial intelligence

🌿 VEGDOG - Germany - EUR 3.5M - Series A - Vegan, gluten-free dog food meeting nutritional needs

🌿 Vow - Australia - $ 49.2M - Series A - Cultivated meat, including exotic (e.g. kangaroo)

🌿 Celleste Bio - Israel - Undisclosed - Seed - Cell-cultured cocoa ingredients

🌿 From Sand to Green - France - $ 150k - Preseed - Capturing CO2 with fruit and biofuel plantations irrigated by desalination of seawater, powered by sun

🌿 Happy Ocean Foods - Germany - Undisclosed - Crowdfunding - Sustainable, plant-based fish and seafood

🌿 Cricularise - Netherlands - EUR 11 m - Series A - blockchain-based traceability platform for supply chains

🌿 Do you like what you are reading? Why not share it with your network?

Resources

🧩 Capacitor is a free worldwide database of microbial fermentation capacity

🧩 A list of Top 100 Alternative protein startups to watch

🧩 Frontier is publishing a database highlighting 100+ critical research and innovation gaps in carbon removal—the CDR Knowledge Gap Database.

🧩 Special focus: CLIMATE TECH HIRING

👉 Database covering all the climate tech VCs portfolio job postings.

👉 Do you want to pivot your career into climate tech? Find here all the relevant communities, job portals, podcasts & fellowships!

👉 Engineers are quitting for climate tech. There is a huge migration happening and the motivation is clear: You can be proud of what you’re doing and not ashamed anymore.

News, Readings & Interesting Things

📗 Great read by Pale Blue Dot VC about Fungi based material. There is hardly a bio-based material source with as many potential applications as fungi.

📗 A great read about the Industrialization of biotech vs. the biologization of industries.

📗 The big news of last week. Read the actual FDA’s answer to Upside Foods stating that the company’s cell-cultured meat is “as safe as comparable foods produced by other methods and would not contain substances that adulterate the food”.

Will this make the whole industry be more optimistic on when cultivated meat hits the super market shelfs? 15 years of claims for when cultivated meat will reach the market

📗From science to CPG. A series examining the challenges food tech companies face when scaling up.

📗 Israeli AgTech map from Startup Nation Central:

📗 A report on Agri-Food ecosystem in Germany by BCG and German Agrifood Society:

📗 Top 20 FoodTech startups of 2022 in Spain:

📗 Revere, a startup by Angellist executive, Eric Woo, is trying to introduce a rating system to the VC industry. Their goal is to create a more standardized way to track information about fund managers so that institutional investors know how to navigate the shifting landscape. They strive to produce PDF reports on funds within a 11-day timeframe, working as a team of 15.

---

Thanks for reading. Reach me with tips, questions, and feedback on Twitter or LinkedIn.

If you enjoy this newsletter, why not share it with your friends and co-workers?

---

___

The views expressed here are my own. All information contained in here has been obtained from publicly available information. While taken from sources believed to be reliable I have not independently verified such information and make no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. For questions simply reply to this email.