These are key operationalized questions from our Cultivated Meat Pivotal Questions project. We want to elicit expert and stakeholder beliefs — before, during, and after reviewing the TEA evidence — to see how views evolve and where consensus exists or doesn't. See our resources page for the key papers informing these questions. (All questions are optional.)
New: Watch the beliefs form walkthrough video for a guided tour of these questions before you fill this in.
Sharing policy: Response content will be shared within the participant group and published publicly. You can choose whether to keep your individual responses anonymous (not attributed to you by name) or allow others to see your name alongside your response — simply leave the name field blank to remain anonymous.
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Canonical PQ questions (Coda)
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For more detail, context, and the complete set of operationalized questions, including those we're commissioning our PQ evaluators to address, see the
canonical PQ formulations on Coda → (use the PQ filter bar and select "cell cultured meat price and cost" to see CM-specific questions)
You don't need to be a specialist to contribute. We want your honest assessment and reasoning, whether you feel highly confident or very uncertain. Your input helps us understand the range of views in the field.
Several CM cost questions are also live on Metaculus for public forecasting.
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Public forecasting on Metaculus; Animal Futures Tournament
[?]Metaculus provides a public interface for a broader pool of forecasters. At least one CM question is going into the animal welfare impact forecasting tournament. Metaculus also allows more detailed belief distributions than the form below. Your Metaculus forecast feeds into our aggregation — please share your username in the form below if you forecast there.
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The focal cost question is live on Metaculus: CM_01 — production cost per kg →. The expert-aggregation version is at CM_03 →. If you forecast on Metaculus, please share your username below so we can link your contributions.
You may also be interested in the Animal Welfare Futures forecasting tournament → we co-launched with Metaculus and Sentient Futures — $3,400 in prizes and only about 25 forecasters as of June 26. It includes some cultivated-meat questions (but not the specific cost questions here), alongside other animal-welfare-relevant questions you may find interesting.
How to respond
- For cost estimates: Give your median estimate and your uncertainty bounds (p10 and p90). [how to form them]Recommended order (Anca Hanea, IDEA protocol): think about your lower bound first — all the reasons costs could be small — then your upper bound — all the reasons costs could be large — and then the median. Starting with the median and adding bounds afterwards leads to intervals that are too narrow. See the guidance fold below for the full approach.
- For probability questions: State your best calibrated subjective probability. (See "How should I think about probability estimates?" below.)
- For open-ended questions: Even a sentence or two helps. We're looking for reasoning and considerations, not polished analysis.
- For numeric fields: Placeholder text shows format only — please form your own estimate before entering a value.
- You can return and update your responses as your views evolve.
Please estimate independently of the cost model. Don't look up what the model currently assumes and enter that — we would just be recycling our own starting point. Your own knowledge, judgment, and calibrated uncertainty is what's informative. [if you do use it]If you do open the model and use its sliders or central estimate as a reference, that's fine — but please note this in your reasoning field or a Hypothes.is comment, and explain what you changed and why. A comparison between your independent view and the model's assumption is useful information too.
▸ Guidance on forming your estimates and uncertainty bounds (Anca Hanea) (unfold)
Anca Hanea (U Melbourne), expert in structured expert elicitation and developer of the IDEA protocolIDEA: Investigate, Discuss, Estimate, Aggregate — a structured protocol designed to extract calibrated expert judgements and combine them while minimising anchoring, groupthink, and overconfidence. See: Hanea et al. (2021), Risk Analysis., recommends this order to avoid anchoring and overconfidence when forming a credible interval:
- Lower bound first (your p10): Think of all the reasons this value might be small — technological breakthroughs, optimistic scenarios, best-case conditions. What is a plausible floor? This is the number below which you assign only a 1-in-10 chance of the true value falling.
- Upper bound second (your p90): Think of all the reasons this value might be large — setbacks, unexpected hurdles, worst-case scenarios. This is the number above which you assign only a 1-in-10 chance.
- Median last: Having fully engaged with both extremes, find your balance point — the value for which you judge an even chance that the true outcome is above or below.
Starting with the median and adding bounds afterwards tends to produce intervals that are too narrow (anchoring + overconfidence). When forming each bound, ask: "What factors could drive this much higher or lower — and how variable are those factors?" Fold that variability into the bounds. If there are things you genuinely don't know, fold that epistemic uncertainty in too — wider bounds are the honest reflection of your state of knowledge.
This form is a lightweight structured survey — not a formal expert elicitation protocol.
Definitions & basis (shared across all questions)
All questions below use these definitions. Click any item to expand. Unless stated otherwise, all values are in inflation-adjusted (CPI) 2025 US dollars, the focal year is 2036 (as of December 31, 2036), and quantities are measured for undifferentiated cultured chicken cell biomass on a wet-weight, at-harvest basis.
▸ Cultured (chicken) meat & the "before any mixture" basis (unfold)
Cultured chicken cell biomass: Undifferentiated (proliferating — actively dividing, in the bioreactor scale-up phase before any directed differentiation into muscle, fat, or connective tissue) chicken cell mass produced in bioreactors, measured before any blending with plant-based or other non-cellular ingredients, and before texturization or downstream structuring. This is the chicken-imitating cultured product in its pre-formulation form.
Before any mixture with plant products: animal cells are often later mixed with plant-based inputs (hydrolysates, plant protein) to reduce total product cost. We focus on the pure cell-biomass cost because it is (1) the technically challenging and costly component, and (2) the quantity most published TEAs estimate; blended product costs can be derived from it given a mixing ratio and filler cost. "Edible kg before mixture," "wet-weight cell biomass at harvest," and "pure cell mass" all refer to the same accounting object at the factory gate.
Scaffold assumption: assume harvested biomass contains minimal non-degraded scaffolding (<10% w/w). If your estimate assumes scaffold-based production, note it and base your cost on the cellular fraction only.
Why chicken? Among cultured meat products, chicken has the highest stakes for animal welfare. Forecasters may base estimates on bovine or generic-mammal TEAs and explain their conversion method.
▸ Average Production Cost (AC) (unfold)
Average Production Cost (AC) = (Annualized capital charge + all operating costs) ÷ annual kilograms of cultured chicken cell biomass (wet weight, at harvest).
- Capital charge: total capital investment (bioreactors, facility) amortized over plant life via the Capital Recovery Factor, adjusted for financing costs (WACC).
- Operating costs include: basal media (amino acids, glucose, vitamins, buffers), recombinant growth factors, utilities, consumables, labor, maintenance, and plant overhead.
Does not include: downstream scaffolding for structuring; texturization; blending with plant-based or other non-cellular ingredients; packaging; distribution; retail markups; R&D amortization; regulatory approval costs; marketing; or profit above capital costs.
Why average production cost? Competitive markets drive prices toward the minimum of the long-run average total cost curve. We ask for your median estimate (50th percentile) across the range of plausible 2036 technology scenarios.
▸ Wet weight, at harvest & undifferentiated cell biomass (unfold)
Wet weight, at harvest: the mass of cells as harvested from the bioreactor (after separation from spent media, before any further processing), including water — typically ~75–90% water depending on cell line, density, and post-harvest dewatering. We use 80% as our reference assumption. If your estimate assumes a different hydration, please note it in your reasoning.
Undifferentiated cell biomass: proliferating cell mass prior to any directed differentiation step — what most published TEAs (Humbird 2021, Pasitka et al. 2024, CE Delft 2021) model. Cost estimates for differentiated muscle fibers (structured whole cuts) would typically be higher; these questions focus on the proliferation/scale-up phase.
▸ Large-scale plants, value units & target years (unfold)
Large-scale plants: plants producing more than 2 kt/yr (2,000 metric tons per year). We exclude smaller experimental/R&D facilities. Cost questions are conditional on large-scale commercial production being achieved — i.e. they ask about unit cost, not whether scale-up happens.
Value units: inflation-adjusted (CPI) 2025 US dollars.
Target years: Very-short-run: end of 2026 · Short-run: December 31, 2031 · Medium-run: December 31, 2036 (focal year) · Longer-run: December 31, 2051.
Why these years? They match Rethink Priorities' forecasting horizons, allowing harmonized comparison; 2051 represents a ~30-year terminal horizon at an efficient optimum.
▸ How should I think about probability estimates? (unfold)
When we ask for a probability, we mean your best calibrated subjective probability — your honest credence given everything you know. One way to think about it: imagine an ideal research team with unlimited resources and data; what probability would you assign that they would ultimately conclude the statement is true? Avoid anchoring to "0% = impossible" / "100% = certain"; a wide interval is more honest than false precision.