Guest Post: The Biggest FoodTech AI Revolution Is Happening Out of Sight

Lore De Valck is a Programme Manager at biotope by VIB, an early-stage investor and accelerator supporting biotech startups with a positive impact on planetary health. In this role, she contributes to biotope’s work helping early-stage founders grow their ventures within a dedicated biotech ecosystem. Her experience encompasses biotech entrepreneurship, strategy consulting and life sciences innovation, including experience at Bain & Company and KQ Labs, the Francis Crick Institute’s accelerator for data-driven health startups. Lore holds an MSc in Bioscience Entrepreneurship from University College London.

In this piece, Lore De Valck explores how artificial intelligence is reshaping foodtech innovation behind the scenes, from accelerating strain and seed development to sharpening the process economics that investors now expect from biotech startups.

Visible AI is only part of the story

Artificial intelligence is becoming a frequent topic in food industry conversations. Most visible applications sit close to the consumers: personalised nutrition apps, demand forecasting tools, automated quality control, production optimisation and recommendation engines. They help reduce waste, improve operational planning and help businesses respond more quickly to changing consumer demand. For plant-based, alternative protein, and sustainable food ingredient companies, the more consequential role of AI may be less visible but no less impactful.

AI platforms are capable of processing large amounts of data supplied and generated through continuous monitoring and adaptation and can make more accurate decisions and real-time predictions for complex biology. All of this can help biotech startups to shorten development timelines, a key sector challenge, by focusing resources on the right experiments, earlier in the process and implementing high-throughput screening..

Speeding up biotech innovation

AI is starting to change how food innovation happens, not by replacing science but by narrowing the guesswork. Instead of testing everything, companies can use large biological and chemical datasets to focus on the most promising compounds, strains, or ingredients from the start. This makes early R&D more targeted, shortens feedback loops, and helps reduce both time and cost, while still relying on real-world lab validation.

This is already evident in areas such as crop and strain development. Companies like Rainbow Crops combine AI with genome engineering to identify complex genetic strain combinations linked to yield, resilience and crop performance. Meanwhile, Bayer has developed a digital twin modelling system that simulates the performance of different seed strains under thousands of environmental conditions before they are ever grown. According to Bayer, this has reduced seed breeding cycles from five or six years to about four months, and is expected to more than double their rate of genetic innovation by the end of the decade.

The pattern is clear: better predictions upfront lead to faster iteration downstream. This applies across precision fermentation, cultivated inputs, seed strain development, and microbiome-related innovation. These are complex biological systems where small changes in strain, feedstock, process conditions, or formulation can materially affect performance. 

Demonstrating stronger process economics

For startups in the foodtech space, scientific progress alone is not enough.  Investors and strategic partners are no longer rewarding novelty in isolation. They want to see clear evidence that a technology can scale, compete on costs and deliver real customer value. 

Investors and strategic partners want to see AI creating a measurable operational advantage. Useful questions for founders and investors include: 

• Does the system reduce development cost? 
• Does it improve yield or performance? 
• Does it help the company reach price parity faster?
• Does it enable an ingredient or product that would otherwise be too complex to develop?

If the answer is no, the AI layer is likely superficial. If the answer is yes, it becomes a real advantage.

The startup advantage

Large corporates have the resources to invest in AI, but much of the real momentum is coming from startups.

Startups are built to experiment. They can test new approaches quickly, iterate, and focus on very specific problems. We’re also seeing a new type of company emerge: startups that don’t develop the end product, but the intelligence layer behind it. By offering AI capabilities as a product, they enable others across the ecosystem to innovate faster.

British startup Elaniti, for example, has developed an AI-powered simulator for soil, digitally recreating a myriad of parameters to simulate how soil biology and composition impact crop performance to provide agribusinesses with predictions about crop performance, nutrient supply and disease susceptibility. As a startup, Elaniti concentrated on offering a specific capability to other agribusinesses, whereas an established multinational agribusiness would have had to invest a significant sum into a proprietary AI platform and potentially would not have risked doing so. 

Science still comes first in the AI Era

In a market where many startups claim to use AI, the relevant question is not whether AI is present. The question is whether it materially changes the outcome. For food businesses, that outcome may be a better ingredient, a more efficient fermentation process, a faster product development cycle, or a clearer route to scale.

The future of foodtech will not belong simply to companies using AI most visibly. It will belong to the startups that use AI to work more efficiently with time, capital, and resources. Some of AI’s biggest foodtech success stories won’t be visible to consumers. They may be behind the scenes helping build the next generation of sustainable foods, ingredients, and health solutions, but that is just what the world needs and demands today.