Do Farmed Meat Forecasts Accurately Model the Risk of Misinformation?
Last week, GFI published their annual industry reports on the three biggest pillars of alt-protein technology, plant-based meats, fermentation, and cultured meats (and teased a fourth).
Like many, I find the reports valuable, as they are a good synthesis of current science, regulation, investment, and product development in all areas.
These reports include analysis of various industry forecasts, aggregated opinions from research houses, industry analysts, and financial analysts. These forecasts for the alternative protein sector span more than three decades and range from fairly consistent (Jeffries at ~$90 billion by 2040) to some bordering on optimism (Credit Suisse’s top forecast at $1.1 trillion by 2050).
As a former industry analyst, I appreciate the difficulty of predicting the future of modeling industries, especially those, like the other protein market, that are still in their early stages. Each of the three pillars of alt-protein fall into slightly different stages of their development: Plant-based meats can still be described as in the early stages of the market, and new types of fermentation-based alt-proteins (such as precision fermentation) are still emerging. Cell-based proteins, with the exception of some early trial releases, are not widely available on store shelves as of early 2023.
Market forecasting models naturally involve thinking about various industry growth factors and inhibitors. In the report, GFI summarizes a few general forecast assumptions:
- Taste and price balance are important.
- Consumer adoption is a factor limiting the growth of the market.
- Innovation begets innovation, investment begets more investment.
While GFI examines each in detail, I will focus here on them consumer acceptance as a factor limiting market growth. It is clear that the market needs consumers, and if they do not take the product, no one can sell it to them.
In the report on the cultured meat market, GFI opens its analysis of this assumption with the following:
Many protein market forecasts see growth as dependent on consumers seeking and purchasing alternative protein products, with market penetration following naturally. Jeffries, for example, points out consumer preference and adoption as key factors for market growth, and the Boston Consulting Group says that growth depends on convincing consumers about taste, texture, and price competitiveness relative to conventional meat.
So far so good. I think Jefferies is right that consumer preference and discovery is the key to growth, and BCG is also right that consumers should see the taste, texture, and price of these products as equal to animal products. GFI agrees that the perception of consumer taste and overall acceptance is important but adds that it is also important for the industry to reach the scale required to meet consumer demand, which is closely related to price, given that price is a major component of supply and demand.
However, what the GFI report and various publicly available documents from BCG or Jefferies do not attempt to assess or quantify the increased risk in the protein industry is different from industry and misinformation related to the product. Disinformation refers to conspiracy theories, incomplete facts, and intentionally misleading information that is spread daily on social media. Those who oppose the new types of proteins quickly increase the volume of false information.
Here is an example from this week:
The above tweet – which has been retweeted 10 thousand times and viewed a million times – includes a false headline from a site (People’s Voice TV) known for traffic in misinformation. The article refers to an article in a publication called Naturalnews.com, which is loosely based on a Bloomberg piece (itself posted above) about the use of so-called immortalized cells in meat-based stem cells such as UPSIDE and Eat Just. Although the Bloomberg article does not say anywhere in it that these cells have been proven to cause cancer, that did not stop People’s Voice TV or tens of thousands of people on Twitter to spread the false story that these products cause cancer and – somehow – that Bill Gates is involved as part of a big conspiracy to control by using… a new kind of meat.
Doesn’t analyzing the proliferation of misinformation about the industry and its products make the GFI analysis or industry analyst reports bad? Not really. Traditional climate models contribute to the basic assumptions surrounding growth drivers and inhibitors and often look at existing market analogs, such as the traditional meat industry in this case, to make assumptions about potential market size, homicides, replacements, etc.
However, by not talking to them, I believe industry experts are underplaying that this industry, especially the cell-based meat market, is stillborn. One only has to look at the significant impact of the wave of negativity on the COVID-19 vaccine to see the power that misinformation (or disinformation) can have on cell-derived meat. Much of the language used by detractors of processed meat is reminiscent of some of the wildest anti-vax programs, often sharing the same anti-science or “bad sponsor” approach.
While I don’t have a definitive answer on how they should account for impact, I can point to some types of risk analysis frameworks that industries and organizations use to measure future risk. Cybersecurity or national security agencies often focus on documenting all potential risks to an organization or business. One example of a type of risk assessment model is that created by NIST, the US Department of Commerce’s security risk assessment.
This is just one example. There are many others, often focused on IT or national security risk, that have described risk assessment and measurement methods. Many of them are designed to obtain a number, in the form of lost business or monetary value, of the organization based on risks.
In the NIST framework, they look to identify all potential sources of threats and events, identify organizational or industry risks, determine the likelihood of occurrence, magnitude of impact, and assess the risk. If this was used in the case of other proteins, it would be relatively easy to use this framework and identify the number of dangers of inaccurate information from various sources. Whether it’s organized groups like traditional animal agriculture groups, politically motivated actors trying to stir up sympathy for a cause, or social media influencers spreading misinformation memes, it’s best to be aware of where misinformation is coming from and use risk analysis to prepare and brace yourself against it.
Others in this space have told me that they don’t want to give these kinds of tropes oxygen; therefore, it is better to ignore them. While I see their point, I’m not sure that ignoring them is the best strategy for long-term survival. Social media has a way of giving oxygen to bad information, so the best response is to identify threats early and develop strategies to deal with them. Although I do not have all the answers, I think that promoters of other proteins need to be prepared for the coming wave of false information, and the best way to do that is to try to calculate its impact and develop strategies to deal with it directly.
