What the modeling hack is ¶
The May 8 2026 workshop elicited 16 real belief responses on cultivated-meat cost and feasibility (plus pre-workshop expert feedback and qualitative discussion). The headline question — CM_01, projected 2036 cost per kg of cell biomass — drew submitted medians spanning roughly $1 to $100/kg. The workshop summary is explicit that this ~100x spread is genuine disagreement about which technical pathways succeed, not a calibration artifact.
The modeling hack is the follow-on, with two goals. First, independent model generation: small quant teams — and AIs — each independently build a model of a specific target outcome (here, the 2036 cost per kg), so we can compare independent models and see how much of the $1–$100 spread is driven by parameters versus model structure. Second, engaging technical experts (GFI, ACIB, Ivy Farm and others, alongside forecasters and informed skeptics) in that modeling — informing the parameters, the context, and the model structure, and building a sense of how to hand their expertise to modelers and AIs in future. You are not asked to become a modeler; you are asked to make your expertise modelable.
The shared job is to decompose why estimates disagree and make each crux explicit, parameterized, and testable — improving the actual cost models (the Python Monte Carlo dashboard and the Squiggle cost model) rather than just re-polling the headline number.
Deliverables we are aiming for
- An updated, version-controlled TEA model (Python + Squiggle) where each contested assumption is an explicit, documented parameter with a credible interval.
- A short written "crux map" linking each modeling disagreement to the belief responses and the literature, with the probability branches that drive the cost spread.
- At least one new worked sub-model for a currently under-modeled driver (e.g. feed-conversion ratio / mass productivity, or product inclusion rate).
- A record of where experts and modelers actually converged versus where the disagreement is irreducible given current evidence.
The cruxes to attack ¶
These are the disagreements the hack should target. Each is a place where a single modeling choice swings the projected cost by a large factor.
1. Cell-line / growth-factor independence
Will gene-edited or autocrine lines reach commercial-scale GF independence by ~2030–2033? If yes, the entire growth-factor cost line collapses (some submitters assign $0/kg GF by 2036). Counter-caveat: editing imposes metabolic burden, so independence is not free.
2. Hydrolysates as base media
Can plant/yeast hydrolysates fully replace purified amino acids (FEASTS, Fuchs) — or only partially (Swartz ~20%, Bomkamp ~40%)? Note CM_12's wording conflated replacing growth factors vs replacing the amino-acid base; part of this crux is definitional.
3. The unit the model is built on
g/L vs cells/mL vs "performance-to-cost ratio" vs mass productivity (kg/m³/day). Several experts argue harvest density alone is "meaningless." The metric a model is built on is a crux — and it ties density to process mode (fed-batch / perfusion / continuous).
4. Feed conversion ratio
Roughly 3:1 kcal in/out lower bound (Swartz); ~20 L/kg used by an industry submitter; "$/L × L/kg effectively IS an FCR" (Bomkamp). Currently a text field only — a prime candidate to add as an explicit parameter.
5. Capital & CDMO-to-dedicated-plant
Can companies finance the path to the 2036 scenario post-2024? CAPEX is flagged as less certain than ingredient costs; timeline and company survival may bind harder than the technical cost floor.
6. What product are we costing toward?
The inclusion rate of CM biomass in the final product is unspecified. $50/kg biomass is fine for a 1.2%-cell hybrid but not a whole cut. Differentiation and end-product attributes are currently out of scope.
Lower-tension but useful as calibration checks: food-grade vs pharma-grade media share (CM_17, near-consensus "low-hanging fruit"), and the GF cost-reduction pathway probabilities (E6), which diverge sharply and are a clean target for structured probabilistic elicitation.
Parameter jam: run a cost simulation in your browser ¶
A simplified, self-contained version of the cost model. Enter your low / mode / high estimate for each cost line (in $/kg of wet cell biomass at harvest), and the page samples ~5,000 Monte Carlo draws and sums them to a unit-cost distribution. This is a teaching toy — the real models have far more structure (process modes, maturity coupling, CAPEX from working volume) — but it shows how component uncertainty compounds. Try a preset, then drag the numbers.
Think of this as the shared baseline: it varies the numbers inside one fixed structure, so it measures parameter sensitivity only. The other — often larger — source of disagreement is the model structure itself (see parameters vs structure below).
Each line is sampled as a triangular(low, mode, high) distribution (clamped so low ≤ mode ≤ high and all ≥ 0); draws are summed per run. Triangular is used for transparency, not because it is the right shape — the production models use lognormals and betas with explicit p5/p95. Runs entirely in your browser; nothing is sent anywhere.
Where these default ranges come from
The defaults are component $/kg ranges drawn from the v0.3 Squiggle model and the Python dashboard, expressed directly as cost-per-kg lines so they sum without the intermediate $/L × L/kg build-up:
- Media (basal + hydrolysates/pharma-grade): beliefs field CM_14 override is roughly p10 ~$5, mode ~$25–40, p90 ~$80–120/kg biomass.
- Growth factors: beliefs field CM_13 override is roughly p10 ~$2, mode ~$10–30, p90 ~$60/kg — collapses toward $0 under the breakthrough/GF-independence regime.
- Other variable (utilities, consumables, supplemental proteins, waste): ~$0.3–3/kg.
- Annualized capital ($/kg): derived in the full model from working volume, WACC, CRF and Lang factor; here entered directly as a per-kg line.
- Fixed OPEX: ~$1–6/kg at a 20 kTA reference plant.
The CDMO-toll, bundled-media, downstream-processing, and process-mode structure of the real models are intentionally omitted here.
Parameters vs structure: the path-dependence test ¶
The jam above varies the numbers inside one fixed additive structure. It cannot show the bigger source of TEA disagreement: the structure — which cost components exist, how they compose, what is bundled or conditional. The published gap (Humbird ~$21/kg, CE Delft ~$6–8, Risner $44,500+) is mostly structural, not a quarrel over inputs.
The test for path dependence: hold the same reconciled parameter sheet fixed, have 2–3 people each build an independent model structure, then overlay their output distributions on one axis. The spread between independently-built models — at identical inputs — is the path-dependence signal: how much of the cost uncertainty is the modeler's structural choices rather than the data.
This is also the role split. Parameters / process experts own one basis-locked, common-currency parameter sheet (the contract); modeling people each build a structure that consumes it identically. Build those independent structures in the forkable tools below, then bring them back to compare.
Where this is heading. The aim is an interface where you describe a model however you like — prose, a sketch, a spreadsheet — and see it rendered back as a diagram, equations, and a plain-language summary, with AI-generated clarifying questions to pin down what you meant. The point is that every model people build lands in a mutually intelligible, comparable form that provably matches the author's intent — which is exactly what lets independent models be compared rather than collected as incompatible forks. AI-assisted Squiggle is one possible substrate; the requirement is the describe → represent → check-intent loop, not any particular language.
Take it further: the real models ¶
The browser toy above sums five hand-entered lines. The production models do much more — process-mode mixtures, a latent industry-maturity factor that couples adoption probabilities and financing costs, CAPEX derived from required working volume, and regime switches for cheap-vs-expensive growth factors. Start here:
Python Monte Carlo dashboard →
Quarto + Shinylive. ~30,000 runs; the authoritative implementation. Adjust parameters and watch the cost distribution move. (model limits & critique)
Squiggle cost model (v0.2/v0.3) →
Live on Squiggle Hub. Fork it, change a distribution, and share your scenario. Best for quick what-ifs and probabilistic reasoning.
Live embed of the Squiggle model (interactive — may take a moment to load):
If the embed above does not render in your browser, open the model directly: cultured_meat_improved_by_squiggle_improve on Squiggle Hub →
New to this? Learn the tools ¶
You do not need to already know how to build Monte Carlo models, write model code, or forecast well to take part. The hack is deliberately mixed: cultivated-meat domain experts who may never have built a model, EA/quant modelers who may not know the biology, and skeptics who want to pressure-test both. Upskilling is a first-class goal, not an afterthought. You will not be asked to learn any modeling syntax — you describe the model you have in mind and AI tooling turns it into a working, checkable model. We will run short, friendly onboarding sessions so domain experts can join the modeling and modelers can engage the biology. Come as you are.
Monte Carlo basics
What "sampling a distribution 5,000 times and summing" actually does, and why uncertain inputs compound. The parameter jam above is a live, hands-on example — start there.
Session: a 20-minute walk-through of the toy simulator and the production overview.
Describe a model — let AI build it
You don't need to learn Squiggle (or any) syntax. Describe the model structure you have in mind — in words, a sketch, or a spreadsheet — and AI tooling turns it into a working, runnable model you can check and adjust. The skill is being clear about structure — what the components are and how they combine — not coding.
Tools: AI-assisted Squiggle (Squiggle AI), or any general AI coding assistant for a Python/spreadsheet model. Session: describe one cost structure aloud and watch it become a model you can interrogate.
Techno-economic cost modeling
How a $/kg figure is built from media volume, cell density, reactor cost, CAPEX annualization, and fixed OPEX — and which assumptions move it most.
Start: the model methodology & parameter reference and the TEA comparison.
Calibrated forecasting & good credible intervals
How to give a 90% interval you would actually bet on — wide enough to be right ~90% of the time, narrow enough to be useful. Most people are overconfident; practice fixes it.
Start: CM_01 on Metaculus · practice with Quantified Intuitions calibration or the Calibrate Your Judgment app. Session: a quick calibration round before we elicit.
If you would like a particular tutorial, or want to run one, say so in the form below.
Propose a model change ¶
Spotted a parameter that is wrong, a crux we missed, or a sub-model worth adding? Tell us. This feeds directly into the hack agenda.