Cultivated meat faces three big challenges: cost, scalability, and resource use. Growth media, the nutrient solution that feeds animal cells during production, makes up 55–95% of total costs. Some components, like TGF-β, cost over £2.4 million per gram. AI is changing this by creating tailored media formulations faster and cheaper than traditional methods. For example, Multus Biotechnology developed a serum-free medium in just six months, cutting costs while improving performance.
How does AI help?
- Data analysis: Machine learning predicts the best nutrient combinations for specific cells.
- Automation: AI-powered labs speed up testing, reducing timelines from years to months.
- Multi-goal optimisation: AI balances cost, growth efficiency, and resource use.
UK companies like Gourmey and Multus are leading breakthroughs, with some achieving production costs as low as £2.76 per pound of meat. While challenges like data quality and collaboration remain, AI is making cultivated meat more affordable and resource-efficient. Consumer education and trust will be key as the UK prepares for commercialisation.
How AI Improves Growth Media for Cultivated Meat
Artificial intelligence is reshaping how growth media for cultivated meat is developed, tackling complex nutrient optimisation challenges with advanced algorithms. By moving away from traditional trial-and-error methods, AI analyses extensive datasets to pinpoint optimal formulations, all while cutting costs and speeding up timelines. At the heart of this transformation lies data analysis, which drives AI's remarkable impact on growth media.
AI-Powered Data Analysis
AI thrives at processing massive datasets, uncovering patterns that human researchers might miss. Machine learning algorithms examine cell behaviour, nutrient uptake, and growth metrics to predict ingredient combinations that yield the best results for specific cell types. This data-first approach eliminates much of the guesswork, allowing researchers to focus on the most promising formulations.
One particularly effective technique is combining response surface methodology (RSM) with radial basis function (RBF) neural networks. For example, studies on zebrafish cell lines using this method achieved a model efficiency of 0.98, accurately predicting growth rates, costs, and environmental impacts [2].
Automation and High-Throughput Testing
When AI meets automated lab systems, the pace and scale of media testing change dramatically. These systems streamline everything from media preparation to cell culturing and data analysis, enabling faster breakthroughs.
Take Multus Biotechnology, for instance. They developed Proliferum P, an animal-free culture medium for porcine adipose-derived stem cells, in under six months using AI and automation [3]. Compare that to the typical timeline of two to four years for traditional methods [3].
"We've built a process that not only accelerates the media development process, but also customises it to specific cell types."
- Soraya Padilla, Project Lead for Proliferum P, Multus Biotechnology [3]
Proliferum P doesn’t just match the performance of fetal bovine serum (FBS); it often surpasses it. It preserves essential stemness characteristics and supports adipogenic differentiation. This marks a leap forward from Multus's earlier product, Proliferum B, which took nine months to develop [3].
"Our platform doesn't just allow us to match industry standards – it ensures we continuously raise the bar. With Proliferum P, we're delivering a superior product to FBS while demonstrating how AI and automation can transform biotechnology development timelines."
- Cai Linton, Co-founder and CEO, Multus Biotechnology [3]
Multi-Objective Optimisation
AI’s capabilities extend beyond rapid testing - it also excels at balancing multiple objectives. Traditional media development often prioritises cell growth rate alone, but AI can optimise for cost, environmental footprint, and performance simultaneously. This is a game-changer for making cultivated meat production scalable and sustainable.
One cutting-edge tool in this area is digital twins - virtual replicas of cell cultivation processes powered by AI. These allow researchers to conduct thousands of virtual experiments, fine-tuning feed formulations and bioreactor conditions without the expense or time required for physical testing.
For example, Gourmey partnered with DeepLife to create an avian digital twin. This system optimises growth conditions, nutrient density, and even flavour expression in cultivated meat [4]. It integrates vast amounts of 'omics' data, such as gene expression and cellular composition, collected throughout the production process.
"By integrating this data with first-principle models of cell metabolism, the digital twin enables us to run thousands of virtual experiments. This helps us identify the optimal feed formulations and bioreactor conditions to maximise yield, minimise resource use, and enhance the sensory qualities of our cultivated meat."
- Nicolas Morin-Forest, Gourmey Co-founder and CEO [4]
The results are impressive. Gourmey’s 5,000-litre bioreactor system can potentially produce cultivated meat at just £2.76 per pound [4]. Meanwhile, Meatly has slashed culture media costs to £0.24 per litre, with plans to reduce this further to around £0.016 per litre at industrial scale [4].
"Our goal is to tailor the feed and cultivation conditions to the exact needs of our cells. This optimisation increases yield and reduces feed waste, directly lowering our production costs."
- Nicolas Morin-Forest [4]
This multi-objective strategy is crucial, as culture media accounts for 55–95% of the total cost of cultivated meat and is a major contributor to its global warming potential [2].
Benefits of AI-Driven Media for Cultivated Meat
The transition from traditional methods to AI-driven approaches offers a range of advantages, addressing some of the biggest challenges in the cultivated meat industry. These benefits go beyond just improving efficiency - they reshape how cultivated meat can move closer to commercial success.
Cost Reduction
One of the most significant hurdles in cultivated meat production is the high cost of culture media, which can account for up to 95% of production expenses [2]. Traditional optimisation methods, like one-factor-at-a-time (OFAT), are slow, resource-intensive, and rely heavily on trial and error [2].
Dr. Charlie Taylor, Head of Business Development at Multus Bio, highlights the issue:
"What holds back media development is the inefficiency of optimisation, so that's across cost; ingredient quality, potency, stability and sustainability; scalability; and bioprocess productivity [proliferation rate, cell density, differentiation efficiency etc]" [5]
AI, using techniques like Bayesian optimisation and machine learning, significantly reduces the number of experimental trials needed. For instance, Multus Bio integrates AI with image processing tools to assess cell growth rates and morphology, generating richer data without adding extra experimental costs [5]. This approach not only cuts costs but also improves outcomes.
By streamlining the optimisation process, AI accelerates progress, making the development of cultivated meat more feasible and cost-effective.
Faster Development Speed
AI dramatically speeds up the media formulation process by processing large datasets and pinpointing promising formulations with advanced analytical methods [2]. Dr. Charlie Taylor underscores the comprehensive impact:
"Smarter decision-making, more data, and doing more in parallel equals better results, faster. Coupled to cheaper inputs and scale economies, that's the roadmap to low-cost media across the metabolic panoply of cultivated meat cell lines." [5]
This faster iteration is vital for an industry racing to scale production. The global AI market in cultivated meat is forecasted to grow at a 39.8% compound annual growth rate (CAGR) from 2025 to 2034 [6]. AI also facilitates quicker discovery and optimisation of cell strains, further accelerating the path to commercialisation [6].
By enabling rapid testing and refinement, AI supports the creation of scalable, efficient production systems.
Better Sustainability
AI doesn't just improve cost and speed - it also enhances the sustainability of media development. By optimising formulations for factors like global warming potential (GWP), cost, and cell growth rates, AI plays a key role in reducing the environmental footprint of cultivated meat production [2]. Compared to traditional meat, cultivated meat offers up to 78–96% fewer greenhouse gas emissions, 99% less land use, and 82–96% less water consumption [7].
AI also helps identify sustainable, cost-effective alternatives, such as plant-based protein hydrolysates, which improve production efficiency while lowering environmental impact [8].
A notable example is Gourmey’s partnership with DeepLife. Their AI-powered avian digital twin runs thousands of virtual experiments to optimise feed formulations and bioreactor conditions. This ensures maximum yield with minimal resource use. As Nicolas Morin-Forest, Gourmey’s co-founder and CEO, explains:
"The digital twin is an AI-powered virtual replica of our cell cultivation process... By integrating this data with first-principle models of cell metabolism, the digital twin enables us to run thousands of virtual experiments. This helps us identify the optimal feed formulations and bioreactor conditions to maximise yield, minimise resource use, and enhance the sensory qualities of our cultivated meat." [4]
This AI-driven approach not only reduces costs but also improves environmental performance across the production process [2].
Challenges and Future Directions in AI-Driven Media Development
While AI offers promising advancements, the path to optimising cultivated meat production is not without obstacles. These challenges highlight the importance of continuous progress and teamwork across different fields.
Data Availability and Quality Issues
AI systems thrive on reliable, high-quality data, but this is where the cultivated meat industry struggles the most. Limited data availability is a significant roadblock to refining media formulations for cultivated meat production. For instance, a 2020 survey [9] revealed that the industry’s relatively short average operational period of 2.5 years has hindered the collection and standardisation of data, making it difficult to train AI models effectively.
In addition, the quality of available data is often inconsistent. Approximately 31% of manufacturers report problems with basal medium components, which are further complicated by undefined protein hydrolysate compositions and batch-to-batch variations [9]. Adding to the complexity, only 33% of manufacturers either acquire or produce growth factors at food-grade purification levels, which impacts the predictive accuracy of AI systems when dealing with fluctuating component quality.
These issues underline the critical need for collaboration and unified efforts to address data-related challenges.
Cross-Disciplinary Collaboration Needs
Solving these data issues requires input from a diverse range of experts, including AI specialists, biologists, and food scientists. However, integrating these disciplines effectively is no small feat. The cultivated meat sector now includes over 175 companies spread across six continents, supported by investments exceeding £2.5 billion as of 2024 [10]. Bridging the gap between computational techniques and biological processes demands professionals who understand both fields. For example, multi-omics data analysis, powered by AI, provides a comprehensive view of biological systems but also requires teams capable of navigating the technical and biological intricacies of cell cultivation [1].
Encouragingly, collaborative efforts and academic programmes are emerging to connect AI with biological sciences [10] [12]. As ICL Planet aptly states:
"This revolution depends on more than great ingredients; it's built on collaboration across chemistry, biology, agriculture, engineering, and data science." [11]
Looking ahead, research should prioritise innovative technologies for recycling media, utilising waste streams, and engineering growth factors with enhanced properties. For example, cost-reduction models suggest that medium prices could drop to under £0.20 per litre using current technologies [1]. Similarly, a team at Northwestern University demonstrated that a widely used stem cell medium formulation could be produced at 97% less cost than its commercial counterpart [1]. Scaling the production of recombinant proteins and growth factors with microbes, fungi, or plants, as well as sourcing components at food or feed-grade levels, will be essential for cutting costs. Additionally, open-source media formulations will increasingly guide the selection and production of raw materials.
To fully realise AI's potential in this field, the industry must focus on creating unified data standards, integrated platforms, and cross-disciplinary education. Tackling these challenges will pave the way for AI to transform cultivated meat production further.
The UK Perspective: Progress and Consumer Awareness
The UK is at the forefront of cultivated meat innovation, thanks to supportive regulations and a strong infrastructure. Advances in AI, particularly in media formulation, are playing a key role in improving production efficiency. With these developments, the country is setting the stage to bring cultivated meat to British consumers.
In March 2025, the Food Standards Agency (FSA) introduced the Cell-Cultivated Products Regulatory Sandbox, backed by £1.6 million from the Department of Science, Innovation and Technology. This two-year programme includes eight cultivated meat startups, such as Hoxton Farms, Roslin Technologies, and Mosa Meat. Its goal? To simplify and modernise the regulatory process for cultivated meat, which previously could cost as much as £500,000 and take over 2.5 years to complete [13]. This regulatory progress is not just about red tape - it’s about building consumer trust and awareness.
"By supporting the safe development of cell-cultivated products, we're giving businesses the confidence to innovate and accelerating the UK's position as a global leader in sustainable food production." – Sir Patrick Vallance, Science Minister [13]
The UK government’s £75 million investment in sustainable food development highlights its commitment to this growing sector [13]. Companies are already seeing results, with AI integration reducing production costs by up to 40% [14].
Cultivated Meat Shop's Role in Public Education
While AI advancements are driving production efficiency, public education is just as important for bridging the gap between innovation and consumer confidence. As cultivated meat edges closer to commercial availability in the UK, educating the public becomes critical. That’s where the Cultivated Meat Shop - the world’s first consumer-focused platform for cultivated meat - steps in. This platform simplifies the science behind AI-driven media optimisation, helping British consumers understand how these technological advances make cultivated meat safer, more sustainable, and increasingly affordable.
The platform offers clear, accessible explanations of complex processes like AI-powered data analysis and multi-objective optimisation. This approach connects technical breakthroughs to real-world benefits. Surveys show that 34% of UK consumers are open to trying cultivated meat products [17]. However, with public understanding still limited, Cultivated Meat Shop focuses on science-based, easy-to-digest content that explains how cultivated meat is produced and its role in creating a more sustainable food system.
Consumer Trust and Adoption
Building consumer trust is essential for the UK market. While a third of UK consumers are willing to try cultivated meat [15], broader adoption depends on addressing concerns around safety, taste, and nutritional value.
The FSA’s regulatory sandbox programme plays a crucial role in fostering trust by ensuring rigorous safety standards. Professor Robin May, Chief Scientific Advisor at the FSA, highlights the importance of this approach:
"Safe innovation is at the heart of this programme. By prioritising consumer safety and making sure new foods, like cell-cultivated products, are safe, we can support growth in innovative sectors. Our aim is to ultimately provide consumers with a wider choice of new food, while maintaining the highest safety standards." – Prof Robin May, Chief Scientific Advisor at the FSA [13]
The environmental benefits of cultivated meat further strengthen its appeal. Compared to conventional European beef, cultivated meat uses 45% less energy. When produced with renewable energy, it can emit up to 92% fewer greenhouse gases, while requiring 95% less land and 78% less water [15].
Dr Mark Post, founder and CSO of Mosa Meat, reflects on the UK’s leadership in this field:
"These are exactly the kind of public-private partnerships we envisioned when we debuted the world's first cultivated burger right here in London in 2013." – Dr Mark Post, Mosa Meat [13]
Looking ahead, the cultivated meat industry could contribute up to €85 billion annually to the EU economy by 2050 and create as many as 90,000 jobs [16]. With AI-driven media optimisation continuing to lower production costs, cultivated meat is steadily moving closer to price parity with traditional meat - a key milestone for broader adoption.
Ultimately, consumer trust hinges on safety, sustainability, and quality. The UK’s strong regulatory framework, combined with educational efforts like those from Cultivated Meat Shop, provides a solid foundation for the successful introduction of cultivated meat products once they receive approval.
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Conclusion: AI's Impact on Cultivated Meat Production
AI-powered solutions for growth media are reshaping the future of cultivated meat production by tackling some of the industry's biggest hurdles. For instance, Multus Bio has achieved a remarkable fivefold cost reduction with its serum-free formulation, which performs on par with 10% FBS. Even more impressively, they managed to complete this development in just 10 months - a process that traditionally takes 2 to 4 years. These advancements not only slash costs but also pave the way for more sustainable and scalable production methods.
The potential for cost reduction is particularly promising in the UK. Take Gourmey's 5,000-litre bioreactor system as an example - it could produce cultivated meat at just £2.76 per pound [4], a major milestone on the path to matching the price of conventional meat.
This progress also highlights AI's ability to balance multiple goals simultaneously, such as yield, environmental impact, and cost, to optimise production efficiency. Given that culture media accounts for up to 95% of production costs and plays a significant role in the environmental footprint, AI's optimisation capabilities are critical for achieving the industry's sustainability targets [7].
That said, technology alone won't ensure success. As the UK moves closer to making cultivated meat commercially available, consumer trust and understanding will be just as important. AI can enhance production safety and efficiency, but transparent communication is key to building confidence. Platforms like Cultivated Meat Shop have a vital role to play in this effort:
"Effective communication about the food safety of cultivated meat is essential for consumer acceptance."
- GFI [18]
FAQs
How does AI help reduce the cost of growth media in cultivated meat production?
How AI Is Helping Cut Costs in Cultivated Meat Production
AI is making waves in cultivated meat production, especially when it comes to reducing the cost of growth media - the nutrient-rich solution essential for cell growth. By sifting through massive datasets, AI can fine-tune the formulation process, pinpointing the most effective nutrient combinations. The result? Less dependency on expensive ingredients and a significant reduction in waste.
But that’s not all. AI also boosts production efficiency by predicting and adapting to factors like cell behaviour and surrounding conditions. These advancements don’t just make cultivated meat production more affordable; they also open the door to scaling up and making sustainable protein options more widely available.
What are digital twins, and how do they help optimise cultivated meat production?
Digital Twins in Cultivated Meat Production
Digital twins are virtual replicas of physical systems or processes, designed to simulate and analyse them in real time. In the context of cultivated meat production, these models replicate cellular behaviour and growth conditions, giving scientists a powerful tool to experiment with variables like growth media composition and culture parameters - all without conducting physical trials.
This approach offers several advantages. By allowing precise control over the production environment, digital twins help cut costs, accelerate development, and enhance product quality. Researchers can rely on data-driven insights to fine-tune processes, making cultivated meat production more efficient and environmentally friendly.
What are the main data challenges in using AI to improve cultivated meat production?
The cultivated meat sector grapples with major hurdles when it comes to data quality and availability, particularly in the development of AI-driven solutions. One of the primary challenges is the absence of high-quality, standardised data related to cell growth and media formulations - critical components for training accurate AI models. On top of that, data variability across different laboratories further complicates efforts to establish consistent benchmarks.
This lack of comprehensive datasets limits AI's ability to deliver reliable predictions or streamline production processes, ultimately slowing progress in cultivated meat technology. Bridging these data gaps is essential to improving efficiency and enabling the industry to scale effectively.
