Other Pivotal Questions Workshops 🥩 Cultivated Meat (Apr 2026) 🥗 Plant-Based Alternatives (May 2026)

We’ve reviewed this critique carefully

New to this model? Start with the Simplest Model → — a shorter version with only the biggest levers, line-of-sight explanations, and no jargon. You can carry your settings over to this Advanced Model when you’re ready.

Save / share this scenario:

Earlier versions of this model carried a separate

All costs in this model are expressed

he CDMO toll is sampled from a lognormal distribution (default p5 = $4/kg, p95 = $40/kg) representing the range of per-kg fees a future food-grade contract manufacturer might charge. See the CDMO mode section below for a full description.

VOC · CAPEX · F

Return to: Interactive Cost Model | New to this topic? How Cultured Chicken is Made | Audio Review (MP3) | Workshop (May 2026)

Sensitivity check: Users can explore partially-correlated scenarios by: - Setting maturity high but technology probabilities low (tests “scale-up succeeds, tech fails”) - Setting maturity low but one technology probability high (tests “isolated breakthrough”)

New

Cell Density / Media-Use Override Code viewof override_mode_constraints = Inputs.toggle({ label: html`Override process mode constraints <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="When ON: process-mode sampling is bypassed and you can specify density and media-use ranges directly. Useful for experts wanting to model specific bioreactor configurations.">(?)</abbr>`, value: urlBool("override_mode_constraints", false) }) Override process mode constraints (?)override_mode_constraints = false Code viewof density_lo = Inputs.range([10, 100], { value: urlNum("density_lo", 30), step: 10, label: "Cell Density Low (g/L)" }) viewof density_hi = Inputs.range([50, 300], { value: urlNum("density_hi", 200), step: 10, label: "Cell Density High (g/L)" }) Cell Density Low (g/L) density_lo = 30 Cell Density High (g/L) density_hi = 200 What is cell density and why does it matter so much? (click to expand) Cell density (g/L at harvest) determines how much meat you get per liter of bioreactor volume. Higher density means less media per kilogram of product, which directly reduces the largest variable cost. Density Media per kg Typical context 10 g/L ~100 L/kg Current lab scale 50 g/L ~20 L/kg Near-term commercial target 200 g/L ~5 L/kg Optimistic TEA projection This is multiplicative. If media costs $1/L, going from 10 to 50 g/L cuts media cost from $100/kg to $20/kg. Going to 200 g/L cuts it to $5/kg. Cell density is arguably the single most important technical parameter for cost reduction. Current state: Most published data shows 10-50 g/L. Some companies claim higher, but these claims are difficult to verify independently. Lever VC’s 2025 report claims 60-90 g/L has been achieved by “second generation” companies. Whether 200 g/L is achievable by 2036 is a genuine open question. What about bioreactor volume / tank size? (click to expand) Bioreactor volume is another major uncertainty that is currently implicit in this model rather than a direct parameter. The model computes total working volume as: total_volume = annual_output / (density × productivity × 365). It then applies a power-law scaling for CAPEX. But individual bioreactor tank size matters for several reasons: Factor Small tanks (2,000-5,000L) Large tanks (20,000-50,000L) Cost per liter Higher Lower (economies of scale) Contamination risk Lower Higher (single failure = large loss) Mixing/O2 transfer Easier Harder at scale Flexibility More modular Less redundancy Industry precedent Pharma standard Requires new engineering Key debate: Some companies (e.g., Vow) claim to have built 20,000L bioreactors for under $1M in 14 weeks using custom food-grade designs. If true, this dramatically changes the CAPEX picture. Humbird’s analysis assumed pharma-grade bioreactors at $50-500/L. Why it’s not a direct slider (yet): Adding individual tank size would require modeling the number of tanks, contamination batch-failure rates, and the trade-off between scale and reliability. This is a planned enhancement. For now, the Plant Capacity and Cell Density parameters together determine total working volume, and the custom reactor ratio (in full view) captures the pharma-vs-food-grade cost difference. Workshop discussion: This is one of the key cruxes for the upcoming CM workshop — what bioreactor scale is realistic, and what does it cost? Advanced: Media-use multiplier (×) What is this — and why can it be below 1? (click to expand) The model computes media volume per kg as (1000 / density) × multiplier. A value of 1 is traditional batch mode (fill reactor once, harvest); >1 is perfusion (multiple media-volume equivalents flow through during the run); <1 represents media recycling, fed-batch with concentrated feeds, or harvest-side cell concentration. The Learn page walks through all three mechanisms. Why the range changed (April 2026): the default p5–p95 was tightened from 1–10× to 0.5–3.0×. The old floor of 1.0 was too restrictive — the GFI 2023 cost-competitive scenarios assume 8–13 L/kg, which at 60–90 g/L density implies a multiplier of roughly 0.5–1.2. A floor of 1.0 mechanically excluded those scenarios no matter how high you pushed density. The new range covers both recycled/fed-batch (<1) and standard perfusion (up to ~3×); values of 5–10× remain plausible for heavily media-intensive processes but are now a stress-test region rather than the default. Show multiplier sliders Code viewof media_turnover_lo = Inputs.range([0.25, 2], { value: urlNum("media_turnover_lo", 0.5), step: 0.05, label: "Media-use multiplier p5 (low end)" }) viewof media_turnover_hi = Inputs.range([1, 10], { value: urlNum("media_turnover_hi", 3.0), step: 0.1, label: "Media-use multiplier p95 (high end)" }) Media-use multiplier p5 (low end) media_turnover_lo = 0.5 Media-use multiplier p95 (high end) media_turnover_hi = 3 Code // URL state writer: serialize every viewof value that DIFFERS FROM ITS // DEFAULT into ?key=val pairs, then debounce-write to the URL via // history.replaceState. Critical invariant: if every slider is at its // default, the URL stays bare (pathname + hash only) — no query string. // This is required so Hypothes.is can find annotations on the canonical // bare URL; a polluted URL breaks annotation lookup for every visitor. // The writer depends on every viewof name below so OJS re-runs it // whenever any input changes. Reads nothing from urlParams. { // Hard-coded defaults must stay in sync with each Inputs.range() / // Inputs.toggle() declaration above and with the reset_adoption button. const defaults = { simpleMode: true, include_blending: false, blending_share: 0.25, filler_cost: 3, include_capex: true, include_fixed_opex: true, include_downstream: false, cdmo_mode: false, cdmo_toll_p5: 4, cdmo_toll_p95: 40, bundled_media: false, bundled_media_p5: 50, bundled_media_p95: 500, plant_capacity: 20, uptime: 0.90, maturity: 0.5, target_year: 2036, p_fedbatch: 0.20, p_perfusion: 0.50, p_continuous: 0.30, override_mode_constraints: false, p_hydro: 0.75, p_recfactors: 0.5, gf_progress: 50, wacc_lo: 8, wacc_hi: 20, asset_life_lo: 8, asset_life_hi: 20, density_lo: 30, density_hi: 200, media_turnover_lo: 0.5, media_turnover_hi: 3.0 }; const state = { simpleMode, include_blending, blending_share, filler_cost, include_capex, include_fixed_opex, include_downstream, cdmo_mode, cdmo_toll_p5, cdmo_toll_p95, bundled_media, bundled_media_p5, bundled_media_p95, plant_capacity, uptime, maturity, target_year, p_fedbatch, p_perfusion, p_continuous, override_mode_constraints, p_hydro, p_recfactors, gf_progress, wacc_lo, wacc_hi, asset_life_lo, asset_life_hi, density_lo, density_hi, media_turnover_lo, media_turnover_hi }; const usp = new URLSearchParams(); let hasDiff = false; for (const [k, v] of Object.entries(state)) { const def = defaults[k]; let matches; if (typeof v === "boolean") matches = (v === def); else if (typeof v === "number") matches = Math.abs(v - def) < 1e-9; else matches = (v === def); if (!matches) { hasDiff = true; if (typeof v === "boolean") usp.set(k, v ? "1" : "0"); else if (typeof v === "number" && Number.isFinite(v)) usp.set(k, String(v)); } } if (window._urlWriteTimer) clearTimeout(window._urlWriteTimer); window._urlWriteTimer = setTimeout(() => { try { const newUrl = hasDiff ? (location.pathname + "?" + usp.toString() + location.hash) : (location.pathname + location.hash); history.replaceState(null, "", newUrl); } catch (e) { console.warn("URL state update failed:", e); } }, 300); return null; } null

In our sensitivity analysis,

doption, reactor costs, and financing. High maturity = correlated improvements.

→ Open Advanced Model with these settings

How is this cost calculated?

Full formula documentation → Model formulas & metrics Code html`<div style="margin-top:1.5rem; padding:0.8rem; background:#f0f8ff; border:1px solid #3498db; border-radius:6px; font-size:0.88em;"> <strong>Want more control?</strong> The <a href="index.html">Advanced Model</a> exposes all parameters: financing (WACC, asset life), plant capacity, cell density, media-use multiplier, CDMO mode, bundled media pricing, and more. <div style="margin-top:0.5rem;"> <a href="${(() => { const cont=Math.max(0,100-p_fedbatch_s-p_perfusion_s); const p=new URLSearchParams({target_year:target_year_s,p_hydro:(p_hydro_s/100).toFixed(2),p_recfactors:(p_recfactors_s/100).toFixed(2),p_fedbatch:(p_fedbatch_s/100).toFixed(2),p_perfusion:(p_perfusion_s/100).toFixed(2),p_continuous:(cont/100).toFixed(2),include_blending:include_blending_s?1:0,blending_share:(blending_share_s/100).toFixed(2)}); return 'index.html?'+p.toString(); })()}" style="font-weight:600;">→ Open Advanced Model with these settings</a> </div> </div>`

Always included

included

Reference commercial scale

Standard equipment life range

8–20% range Typical food/biotech financing range

Parameter Value Why fixed Industry Maturity 0.5 (neutral) At 0.5 the maturity factor has zero net effect on probabilities or financing

Probability Thresholds Code { function card(thresh, prob, label, color, bprob) { const bc = prob > 30 ? color : '#ddd'; const blend = include_blending_s && bprob !== undefined ? `<div style="font-size:0.8em; color:#1a5276; background:#f0f8ff; border-radius:3px; padding:2px 5px; margin-top:4px;"> Blended: <strong>${bprob.toFixed(1)}%</strong> chance &lt; $${thresh}/kg </div>` : ''; return `<div style="border:2px solid ${bc}; padding:0.9rem; border-radius:8px; text-align:center;"> <h5 style="margin:0 0 0.2rem;">P(Pure cells &lt; $${thresh}/kg)</h5> <h2 style="color:${color}; margin:0.2rem 0;">${prob.toFixed(1)}%</h2> <small style="color:#666;">${label}</small> ${blend} </div>`; } const grid = `<div class="grid" style="grid-template-columns:repeat(4,1fr); gap:0.75rem; margin-bottom:1.5rem;"> ${card(10, stats_s.prob_10, 'could approach conventional chicken (~$5-10/kg retail)', '#27ae60', stats_s.bprob_10)} ${card(25, stats_s.prob_25, 'range where premium cultured products may be viable', '#3498db', stats_s.bprob_25)} ${card(50, stats_s.prob_50, 'potential niche/specialty market', '#f39c12', null)} ${card(100, stats_s.prob_100, 'substantially below current lab-scale costs', '#e74c3c', null)} </div>`; const blendRow = include_blending_s ? ` <p style="font-size:0.88em; color:#1a5276; font-weight:500; margin:0.5rem 0 0.3rem;"> Blended product (${stats_s.bs*100|0}% CM + ${((1-stats_s.bs)*100)|0}% filler at $3/kg) — consumer-relevant prices: </p> <div class="grid" style="grid-template-columns:repeat(3,1fr); gap:0.6rem; margin-bottom:1.5rem;"> <div style="border:2px solid ${stats_s.bprob_5>20?'#27ae60':'#ddd'}; padding:0.8rem; border-radius:8px; text-align:center;"> <h5 style="font-size:0.85em; margin:0 0 0.2rem;">P(Blend &lt; $5/kg)</h5> <h2 style="color:#27ae60; margin:0.2rem 0;">${stats_s.bprob_5.toFixed(1)}%</h2> <small>competitive with conventional chicken</small> </div> <div style="border:2px solid ${stats_s.bprob_8>30?'#3498db':'#ddd'}; padding:0.8rem; border-radius:8px; text-align:center;"> <h5 style="font-size:0.85em; margin:0 0 0.2rem;">P(Blend &lt; $8/kg)</h5> <h2 style="color:#3498db; margin:0.2rem 0;">${stats_s.bprob_8.toFixed(1)}%</h2> <small>competitive with premium chicken/beef</small> </div> <div style="border:2px solid ${stats_s.bprob_12>50?'#f39c12':'#ddd'}; padding:0.8rem; border-radius:8px; text-align:center;"> <h5 style="font-size:0.85em; margin:0 0 0.2rem;">P(Blend &lt; $12/kg)</h5> <h2 style="color:#f39c12; margin:0.2rem 0;">${stats_s.bprob_12.toFixed(1)}%</h2> <small>affordable specialty market</small> </div> </div>` : ''; return html([grid + blendRow]); } TypeError: Cannot read properties of null (reading 'toFixed')

TypeError: Cannot read properties of null (reading 'toFixed')

Blended Product Code viewof include_blending_s = Inputs.toggle({ label: "Show blended product analysis", value: urlBool_s("include_blending", false) })

all parameters

Projected 2036 Cost Distribution:where(.plot-d6a7b5) { --plot-background: white; display: block; height: auto; height: intrinsic; max-width: 100%; } :where(.plot-d6a7b5 text), :where(.plot-d6a7b5 tspan) { white-space: pre; }

A first look at cultured chicken cost — for exploration and understanding

Year

Continuous (auto): 35%

xposes only the biggest levers on cultured chicken

WACC

Background

Parameters

Mix

·

For more complete analysis with all parameters exposed

38.7% chance blended product (25% CM, $3/kg filler) < $10/kg

Parameter Sensitivity: Dollar Swing in Mean Unit Cost :where(.plot-d6a7b5) { --plot-background: white; display: block; height: auto; height: intrinsic; max-width: 100%; } :where(.plot-d6a7b5 text), :where(.plot-d6a7b5 tspan) { white-space: pre; }

Asset Life High

Simplified view: Less pivotal parameters (plant capacity, uptime, financing costs, media-use multiplier) are set to reasonable defaults. In our sensitivity analysis, these contribute less than 10% of the variance in cost estimates. Switch off to adjust all parameters. Code // Reactive style block to hide/show full-mode-only and cdmo-only inputs html`<style> .full-mode-only { display: ${simpleMode ? 'none' : 'block'}; } .cdmo-only { display: ${cdmo_mode ? 'block' : 'none'}; } .override-mode-only { display: ${override_mode_constraints ? 'block' : 'none'}; } .separable-only { display: ${bundled_media ? 'none' : 'block'}; } .bundled-only { display: ${bundled_media ? 'block' : 'none'}; } .blending-only { display: ${include_blending ? 'block' : 'none'}; } </style>` .full-mode-only { display: none; } .cdmo-only { display: none; } .override-mode-only { display: none; } .separable-only { display: block; } .bundled-only { display: none; } .blending-only { display: none; }

Cultured Chicken Production Cost Model CodeShow All CodeHide All CodeView Source

P(Scalable Growth Factor (GF)

Cell Density (g/L)

Which parameters have the most impact on the final cost? Each bar shows the dollar swing in mean unit cost between simulations where the parameter is in its top 10% versus its bottom 10%. Larger bars = bigger levers on cost. Code { const uc = results.unit_cost; // Deduplicated parameter list. // Removed vs. previous version (see explainer below for details): // • L/kg (volume) — deterministic function of density × media-use multiplier // • Uses Hydrolysates — regime-switch subsumed into Media $/L // • Has Cheap GFs — regime-switch subsumed into GF Price / GF Quantity const params = [ {name: "Cell Density (g/L)", data: results.density_samples, kind: "primitive"}, {name: "Media-use multiplier (×)", data: results.media_turnover_samples, kind: "primitive"}, {name: "Media $/L (incl. hydrolysate regime)", data: results.media_cost_L_samples, kind: "mixture"}, {name: "GF Price ($/g, incl. regime)", data: results.price_recf_samples, kind: "mixture"}, {name: "GF Quantity (g/kg, incl. regime)", data: results.g_recf_samples, kind: "mixture"}, {name: "Industry Maturity (latent — see note)", data: results.maturity_samples, kind: "latent"}, {name: "Plant Capacity (kTA)", data: results.plant_kta_samples, kind: "primitive"}, {name: "Utilization Rate", data: results.uptime_samples, kind: "primitive"} ]; const swings = params.map(p => ({ name: p.name, kind: p.kind, swing: conditionalSwing(p.data, uc, 0.10) })); const sorted = swings .map(s => ({...s, absSwing: Math.abs(s.swing)})) .sort((a, b) => b.absSwing - a.absSwing); const maxAbs = Math.max(...sorted.map(s => s.absSwing), 1); const pad = maxAbs * 0.30; const tornadoPlot = Plot.plot({ width: 900, height: 440, marginLeft: 290, marginRight: 100, x: { label: "Δ mean unit cost ($/kg): top 10% − bottom 10% of parameter", domain: [-maxAbs - pad, maxAbs + pad], grid: true, labelOffset: 40, tickFormat: d => (d >= 0 ? "+$" : "−$") + Math.abs(d).toFixed(0) }, y: { label: null, tickFormat: d => d, tickSize: 0 }, color: { domain: ["Increases cost", "Decreases cost"], range: ["#e74c3c", "#27ae60"] }, style: { fontSize: "13px" }, marks: [ Plot.barX(sorted, { y: "name", x: "swing", fill: d => d.swing > 0 ? "Increases cost" : "Decreases cost", sort: {y: "-x", reduce: d => Math.abs(d)} }), Plot.ruleX([0], {stroke: "black", strokeWidth: 1}), Plot.text(sorted, { y: "name", x: d => d.swing > 0 ? d.swing + maxAbs * 0.025 : d.swing - maxAbs * 0.025, text: d => (d.swing > 0 ? "+$" : "−$") + Math.abs(d.swing).toFixed(1) + "/kg", textAnchor: d => d.swing > 0 ? "start" : "end", fontSize: 12, fontWeight: 500 }) ] }); return html`<div style="font-size: 1em;"> <div style="font-weight: normal; font-size: 1.05em; margin-bottom: 0.5rem; color: #333;">Parameter Sensitivity: Dollar Swing in Mean Unit Cost</div> ${tornadoPlot} </div>`; }

Sensitivity Analysis (Tornado Chart)

Technology Adoption & Process Mode (Realized) Code { const isOverride = results.mode_is_override; const pct_fb = (results.pct_fedbatch * 100).toFixed(0); const pct_pf = (results.pct_perfusion * 100).toFixed(0); const pct_ct = (results.pct_continuous * 100).toFixed(0); const modeCard = isOverride ? html`<div class="card" style="border: 1px solid #ddd; padding: 1rem; border-radius: 8px; text-align: center; grid-column: 1 / -1;"> <h5>Process Mode</h5> <div style="color: #888; font-size: 0.9em;">Override — using manual density / media-use ranges</div> </div>` : html`<div class="card" style="border: 1px solid #16a085; padding: 1rem; border-radius: 8px; grid-column: 1 / -1;"> <h5 style="margin-bottom:0.5rem;">Process Mode (realized)</h5> <div style="display:flex; gap:1.5rem; justify-content:center; font-size:1.1em;"> <span>Fed-batch <strong style="color:#e67e22;">${pct_fb}%</strong></span> <span>Perfusion <strong style="color:#16a085;">${pct_pf}%</strong></span> <span>Continuous <strong style="color:#2980b9;">${pct_ct}%</strong></span> </div> </div>`; return html`<div class="grid" style="grid-template-columns: repeat(2, 1fr); gap: 1rem; margin: 2rem 0;"> <div class="card" style="border: 1px solid #ddd; padding: 1rem; border-radius: 8px; text-align: center;"> <h5>Hydrolysates Adopted</h5> <h2 style="color: #27ae60;">${(results.pct_hydro * 100).toFixed(0)}%</h2> <small>of simulations use hydrolysates</small> </div> <div class="card" style="border: 1px solid #ddd; padding: 1rem; border-radius: 8px; text-align: center;"> <h5>Cheap Growth Factors</h5> <h2 style="color: #9b59b6;">${(results.pct_recf_cheap * 100).toFixed(0)}%</h2> <small>of simulations have cheap factors</small> </div> ${modeCard} </div>`; }

average contribution

Component Distributions

Where does the cost come from? This chart shows the average contribution of each cost component across all simulations. The largest bars are the cost drivers to focus on — these are where technological progress or parameter uncertainty has the most impact. Code { const mediaLabel = bundled_media ? "Complete Media (incl. GFs)" : "Media (incl. basal micros)"; const allComponents = [ {name: mediaLabel, value: mean(results.cost_media), color: "#27ae60"}, {name: "Growth Factors", value: mean(results.cost_recf), color: "#9b59b6"}, {name: "Other VOC", value: mean(results.cost_other_var), color: "#7f8c8d"}, {name: "CAPEX (annualized)", value: mean(results.cost_capex), color: "#e74c3c"}, {name: "Plant overhead OPEX", value: mean(results.cost_fixed), color: "#f39c12"}, {name: "CDMO Toll", value: mean(results.cost_cdmo_toll), color: "#e67e22"}, {name: "Downstream", value: mean(results.cost_downstream), color: "#1abc9c"} ]; // Filter out zero-value components (e.g., downstream when not included) const components = allComponents.filter(c => c.value > 0.001).sort((a, b) => b.value - a.value); const total = components.reduce((s, c) => s + c.value, 0); const chartContainer = document.createElement("div"); chartContainer.style.position = "relative"; // Expand/collapse button const expandBtn = document.createElement("button"); expandBtn.textContent = "Expand Chart"; expandBtn.style.cssText = "padding: 0.3rem 0.7rem; font-size: 0.8rem; cursor: pointer; border: 1px solid #ccc; border-radius: 4px; background: #f8f9fa; margin-bottom: 0.5rem;"; let expanded = false; expandBtn.onclick = () => { expanded = !expanded; expandBtn.textContent = expanded ? "Collapse Chart" : "Expand Chart"; chartEl.replaceWith(makeChart(expanded)); chartEl = chartContainer.querySelector(".cost-breakdown-plot"); }; chartContainer.appendChild(expandBtn); function makeChart(large) { const w = large ? 1200 : 1000; const h = large ? 700 : 580; const fontSize = large ? 14 : 13; const p = Plot.plot({ width: w, height: h, marginLeft: 200, marginRight: 140, x: { label: "Average Cost ($/kg)", grid: true }, y: { label: null }, marks: [ Plot.barX(components, { y: "name", x: "value", fill: "color", sort: {y: "-x"} }), Plot.text(components, { y: "name", x: d => d.value + 0.5, text: d => `$${d.value.toFixed(2)} (${(d.value/total*100).toFixed(0)}%)`, textAnchor: "start", fontSize: fontSize }) ], title: `Cost Breakdown by Component (Total: $${Math.round(total)}/kg)` }); p.classList.add("cost-breakdown-plot"); return p; } let chartEl = makeChart(false); chartContainer.appendChild(chartEl); return chartContainer; } Expand Chart

Show blended product cost

Why it matters: If production costs for pure cells reach ~$10/kg, even 100% cultured products could compete with conventional chicken. At $25-50/kg, hybrid products with moderate cell inclusion rates may still reach price parity. If costs remain >$100/kg, even hybrid products face significant price premiums. These thresholds inform whether animal welfare interventions should prioritize supporting this industry. Code html`<div class="grid" style="grid-template-columns: repeat(3, 1fr); gap: 1rem; margin-bottom: 2rem;"> <div class="card" style="background: linear-gradient(135deg, #3498db, #2980b9); color: white; padding: 1.5rem; border-radius: 8px;"> <h4 style="margin: 0; opacity: 0.9;">Median Pure Cell Mass Cost (p50)</h4> <h2 style="margin: 0.5rem 0;">$${Math.round(stats.p50)}/kg</h2> <small>$/kg pure cell mass (wet weight) — half of simulations above, half below</small> </div> <div class="card" style="background: linear-gradient(135deg, #27ae60, #1e8449); color: white; padding: 1.5rem; border-radius: 8px;"> <h4 style="margin: 0; opacity: 0.9;">Optimistic (p5)</h4> <h2 style="margin: 0.5rem 0;">$${Math.round(stats.p5)}/kg</h2> <small>Only 5% of simulations cheaper</small> </div> <div class="card" style="background: linear-gradient(135deg, #e74c3c, #c0392b); color: white; padding: 1.5rem; border-radius: 8px;"> <h4 style="margin: 0; opacity: 0.9;">Pessimistic (p95)</h4> <h2 style="margin: 0.5rem 0;">$${Math.round(stats.p95)}/kg</h2> <small>95% of simulations cheaper</small> </div> </div> ${include_blending ? html`<div style="background: #eaf7ea; border-left: 4px solid #27ae60; padding: 0.8rem 1rem; margin-top: 0.5rem; font-size: 0.9em;"> <strong>Blended product estimate (${Math.round(blending_share * 100)}% CM, ${Math.round((1-blending_share)*100)}% filler at $${filler_cost}/kg):</strong> Median <strong>$${stats.blended_p50.toFixed(1)}/kg</strong> · 90% CI: $${stats.blended_p5.toFixed(1)} – $${stats.blended_p95.toFixed(1)}/kg </div>` : html`<div style="background: #fef9e7; border-left: 4px solid #f39c12; padding: 0.8rem 1rem; margin-top: 0.5rem; font-size: 0.9em;"> <strong>Hybrid product estimate:</strong> At a CM inclusion rate of ~25% with plant-based filler at ~$3/kg, the blended ingredient cost would be approximately <strong>$${(stats.p50 * 0.25 + 3 * 0.75).toFixed(1)}/kg</strong> (median). Enable "Show blended product cost" in the sidebar to adjust these assumptions. </div>`} </div>`

Results Summary Code html`<div style="background: #f8f9fa; padding: 1rem 1.25rem; border-left: 4px solid #3498db; margin-bottom: 1.5rem; font-size: 0.95em; line-height: 1.6;"> <strong>What these numbers represent:</strong> Simulated <strong>production cost per kilogram of pure cultured chicken cells</strong> (<span title="Wet weight = the mass of cells as harvested from the bioreactor, including water content (~70-80%). This is the standard output basis used in most TEAs (Humbird 2021, Pasitka 2024). It does NOT include downstream processing into structured products, blending with plant-based ingredients, or retail margins. For comparison: Humbird reports $37/kg wet cell mass; Pasitka reports $13.75/kg wet cell mass (large perfusion). The widely-cited ~$6/lb Pasitka figure is for a 50/50 hybrid product, not pure cell mass. See our TEA Comparison page for details." style="text-decoration: underline dotted; cursor: help;">wet weight, unprocessed &#9432;</span>) in <strong>${target_year}</strong>, based on ${stats.n.toLocaleString()} Monte Carlo simulations. This is the cost to produce cell mass in a bioreactor — not the cost of a consumer product, and not retail price. <a href="compare.html" style="font-size: 0.9em;">[Compare to published TEAs →]</a> <br><br> <strong><span title="UPSIDE Foods' chicken cutlet is a blend of cultured chicken cells and plant-based ingredients. SuperMeat's chicken burger used ~30% cultured cells. The GFI State of the Industry 2024 report notes that 'hybrid products combining cultivated and plant-based ingredients are the most likely near-term path to market.' Eat Just/GOOD Meat's Singapore-approved product uses cultured chicken in a plant-protein matrix.">Pure cells vs. consumer products:</span></strong> Most cultivated meat products on the market or in development are <em>hybrid products</em> — blending a fraction of cultured cells with plant-based or mycoprotein ingredients. A product with (say) 20% cultured cells and 80% plant-based filler at $3/kg would have a blended ingredient cost far below the pure-cell cost shown here. The "price parity with conventional meat" threshold may therefore be achievable at higher per-kg cell costs than these numbers suggest. <br><br> <strong>Why it matters:</strong> If production costs for pure cells reach <strong>~$10/kg</strong>, even 100% cultured products could compete with conventional chicken. At <strong>$25-50/kg</strong>, hybrid products with moderate cell inclusion rates may still reach price parity. If costs remain <strong>>$100/kg</strong>, even hybrid products face significant price premiums. These thresholds inform whether animal welfare interventions should prioritize supporting this industry. </div>`

Component Distributions

Process Mode Mix Code viewof p_fedbatch = Inputs.range([0, 1], { value: urlNum("p_fedbatch", 0.20), step: 0.05, label: html`Fed-batch weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Low density (5–30 g/L), moderate media use (1–2×). Semi-continuous: nutrient-concentrated feeds added periodically. Less efficient than perfusion.">(?)</abbr>` }) viewof p_perfusion = Inputs.range([0, 1], { value: urlNum("p_perfusion", 0.50), step: 0.05, label: html`Perfusion weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Medium-high density (30–150 g/L), higher media throughput (1–5×). Continuous media exchange with cell retention. Currently the industry standard for high-density CM production.">(?)</abbr>` }) viewof p_continuous = Inputs.range([0, 1], { value: urlNum("p_continuous", 0.30), step: 0.05, label: html`Continuous weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Highest density (50–200 g/L), efficient media use (0.5–3×). Near-steady-state operation; cells grown and harvested continuously with optimized recycling.">(?)</abbr>` })

Fed-batch weight

Process Mode Mix Code viewof p_fedbatch = Inputs.range([0, 1], { value: urlNum("p_fedbatch", 0.20), step: 0.05, label: html`Fed-batch weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Low density (5–30 g/L), moderate media use (1–2×). Semi-continuous: nutrient-concentrated feeds added periodically. Less efficient than perfusion.">(?)</abbr>` }) viewof p_perfusion = Inputs.range([0, 1], { value: urlNum("p_perfusion", 0.50), step: 0.05, label: html`Perfusion weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Medium-high density (30–150 g/L), higher media throughput (1–5×). Continuous media exchange with cell retention. Currently the industry standard for high-density CM production.">(?)</abbr>` }) viewof p_continuous = Inputs.range([0, 1], { value: urlNum("p_continuous", 0.30), step: 0.05, label: html`Continuous weight <abbr style="cursor:help;text-decoration:underline dotted;font-size:0.85em;color:#888;" title="Highest density (50–200 g/L), efficient media use (0.5–3×). Near-steady-state operation; cells grown and harvested continuously with optimized recycling.">(?)</abbr>` })

proven interventions like corporate campaign

The beliefs form is at uj-cm-workshop.netlify.app/beliefs.html. A short pre-workshop version goes out ~May 1. The in-workshop portion is lightweight: we'll orient you to the form at the breaks, and ask for a live response on CM_01 (the focal cost question) and one or two subquestions.

What should The Unjournal evaluate next — which papers, which questions, which specialists? [Suggested discussants]Suggested discussants:Matt McNulty (Tufts CCA) — wants to discuss tea_review + business_environment; strategic view of research priorities from an academic CM centerDavid Manheim (Technion/ALTER) — evaluator; wants to discuss modeling_hack; systematic view on what would be most informative to evaluate

CM_01

Believer Meats published favorable TEA results in Nature Food yet shut down in late 2025.

process choice

EU/CET participants likely available (15:00–16:00 CET):Aleksandra Fuchs (ACIB, Graz) — presenting in S1; likely joining earlyJordi Morales-Dalmau (Cultimate Foods, Berlin) — CET; indicated flexible availabilityMirjam Capuder (Univ Maribor) — indicated 15:00 CET availability on May 8Tom Bry-Chevalier (Univ de Lorraine) — CET; indicated May 8 availableJakub Kozlowski (Zurich

(ideally presented by a participant with direct CM expertise; otherwise DR will walk through the key model inputs and assumptions).

If you are presenting slides in S2 and would like them shared more widely, just let us know and we will circulate them separately.

batch ≈ 1,

Batch vs. Perfusion: Two O

Batch v

Against this, Kalkar argued the opposite — that the contested empirical picture creates an opening: it may make sense to be a first-mover and evaluate now, given Ord's reframed 2027+ predictions"it may actually make sense to be a 'first-mover' and evaluate it now. Given that Toby Ord's reframed his predictions to 2027+, there's a window of opportunity"— Uma Kalkar.

I think engage with funders of AI and catastrophic risk, alignment etc. (I think Schmidt is interested, but are only funding research right now) -- it might be useful to reach out to them to provide an open evaluation of the work that they are commissioning / providing grants to. This way we also get to engage with AI researchers working at the forefront (at least in economics, broadly).

Hypothesis — click the < tab on the right edge of any dashboard page

Fixed

basal media

Assessor prioritization rating

The NBER format implies a working-paper stage

▾ Details

here Animal welfare & food systems High Priority Mar 24, 2026 |EAFORUM gpt-5.4-mini ▾ Details This looks l

Comment directly on this page using the Hypothes.is sidebar (look for the < tab on the right edge of the page). Highlight any text and add your annotation — visible to all Hypothes.is users. You can also use the feedback buttons on each paper card.

GFI amino acid report 2025

$6/lb figure is for a 50/50 hybrid product

Main Caveat

Time horizon: Current technology vs. 2030 projections vs. steady-state

Hydrolysate-based vs. pharma-grade vs. animal-component-free

Pure wet cell mass vs. cultivated ingredient vs. hybrid product vs. retail-equivalent

hicken (Pasitka 2024) vs. generic mammalian/CHO-like cells (Humbird 2021) vs. unspecified (CE Delft 2021)

Basis

Read This First

A latent factor (0=nascent, 1=mature) that affects all technology adoption, reactor costs, and financing. High maturity = correlated improvements.

Cell Density Range Code viewof density_lo = Inputs.range([10, 100], { value: 30, step: 10, label: "Cell Density Low (g/L)" }) viewof density_hi = Inputs.range([50, 300], { value: 200, step: 10, label: "Cell Density High (g/L)" })

Model Parameters Code viewof simpleMode = Inputs.toggle({label: "Simplified view (recommended)", value: true})

Two controls for growth factors:

How far along the price reduction curve we are within each regime:

Component Distributions

Cost Breakdown by Component (Total: $122.59/kg):where(.plot-d6a7b5) { --plot-background: white; display: block; height: auto; height: intrinsic; max-width: 100%; } :where(.plot-d6a7b5 text), :where(.plot-d6a7b5 tspan) { white-space: pre; }

Variable Operating Costs (VOC):

Technology Adoption by 2036

Total: $122.59

Unit Production Cost ($/kg) →

Basic Parameters

$$

Model Structure Code viewof include_capex = Inputs.toggle({label: "Include capital costs (CAPEX)", value: true}) viewof include_fixed_opex = Inputs.toggle({label: "Include fixed operating costs", value: true}) viewof include_downstream = Inputs.toggle({label: "Include downstream processing", value: false})

Pure cells vs. consumer products: Most cultivated meat products on the market or in development are hybrid products — blending a fraction of cultured cells with plant-based or mycoprotein ingredients. A product with (say) 20% cultured cells and 80% plant-based filler at $3/kg would have a blended ingredient cost far below the pure-cell cost shown here. The "price parity with conventional meat" threshold may therefore be achievable at higher per-kg cell costs than these numbers suggest.

GitHub issues: Open an issue Email: contact@unjournal.org

Quick Reference: All Equations at a Glance

The paper’s object is an abstract characterization of strategy-proof social choice rules for selecting a public-good level. While public decision rules can matter in principle, the abstract theorem is not tied to a concrete policy domain, institution, or implementation setting. There is no evident link to a specific decision-maker, welfare question, or operational policy lever where an evaluation would affect choices at scale.

This is a strong Unjournal candidate: it is directly about improving job recommendation systems used by a public employment service, has clear welfare implications for job seekers, and uses randomized field experiments rather than purely predictive metrics. The paper addresses a decision-relevant policy question—how to design algorithms that improve worker outcomes rather than platform clicks/applications—and appears to offer actionable guidance for public and private labor-market intermediaries. As a working paper with experimental evidence and a model-based welfare metric, it has high timing value and likely benefit from independent evaluation.

Following our evaluation of Rethink Priorities' cultured meat forecasting work and ongoing TEA evaluations, this workshop focuses on what the evidence tells us about cultivated meat's production cost trajectory. We recognize that consumer acceptance, regulatory pathways, and environmental implications also matter — but we're centering on costs because this seems among the most pivotal and tractable questions right now, and we want to bring focused expertise to bear. Pivotal Questions Initiative → 📊 Cost Modeling Dashboard → EA Forum: CM Viability → CM_01 on Metaculus → RP Evaluation →

Async Discussion & Suggestions

Or mark your availability on the grid (optional)

Or mark your availability on the grid (optional) Click cells for any time blocks you could join. Click a date to select that row, a time header to select that column, or a week label to select the whole week. All times US Eastern; hover for UK/CET.

Note: This workshop is still in early planning. We're gathering initial interest and availability. Final dates and agenda will be confirmed once we have responses from key participants.

We're continuing the discussion asynchronously and will be publicly sharing key materials soon. This site is evolving into a resource page.

1. WELLBY Reliability and Value

Join the discussion (Google Doc)

Evaluation: Cash Transfers vs Psychotherapy in Liberia (McGuire et al.) Unjournal Evaluation Summary Applied Comparison Direct experimental comparison of cash transfers and psychotherapy in an LMIC context. Particularly relevant because it measures multiple outcomes—psychological distress, consumption, life satisfaction—allowing cross-metric comparison. Evaluation Summary

Essential

The Controversy: Happier Lives Institute estimated StrongMinds a

otentially more cost-effective than AMF. GiveWell's 2023 assessment disagreed, citing concerns about: (1) mapping depression scales to LS, (2) assumed effect duration, (3) demand effects in self-reported outcomes, and (4) publication bias.

Peasgood et al. (unpublished)

Unit-change comparability

📚 Further Reading: Unjournal Evaluations The Unjournal has commissioned independent evaluations of papers relevant to this debate: → StrongMinds & Friendship Bench Evaluation — Critical assessment of HLI's meta-analysis and cost-effectiveness claims → Long-Run Effects of Psychotherapy on Depression — Cuijpers et al. meta-analysis on therapy durability → Cash Transfers vs Psychotherapy: Comparative Impact — McGuire et al. direct comparison in Liberia → Mental Health Therapy as a Core Strategy (Ghana) — Barker et al. on scaling community-based therapy

mortality-focused interventions

Practical guidance for funders now Given the uncertainties above, what should funders actually do? This section offers a decision-oriented framework, not a single prescription.

Zoom chat for quick reactions;

Segment structure is set; timing may adjust slightly. Updated March 11, 2026

calibrated

Consider the value obtained when using the best feasible measure for cross-intervention comparison in contexts like the focal context. What share of this value is obtained, in expectation, from using the simple linear WELLBY measure (as defined above) for all interventions?

Consider the value obtained

We're organizing the discussion around four key questions:

The neutral point is the life satisfaction level representing neither positive nor negative welfare—essentially the boundary between "life worth living" and "suffering." Estimates range from 2-5 on the 0-10 scale. Peasgood et al. (2018) tentatively estimate ~2.

evaluation summary

QALYs (quality-adjusted life years)

instruments like EQ-5D

Other measures include QALYs (quality-adjusted life years), income-equivalent measures, and multi-dimensional poverty indices. QALYs are similar to DALYs but measure health gained rather than lost.

—and what would change their minds?

Unlike WELLBYs, DALYs are based on expert-derived disability weights rather than self-reported wellbeing—weights are constructed through surveys of health professionals rating hypothetical health states.

Vignette exercises: respondents rate hypothetical people's life satisfaction based on descriptions, revealing how individuals anchor the scale and enabling cross-person calibration.

Calibration questions ask respondents to rate well-defined scenarios (e.g., "How satisfied would you be if you won $1,000?"). By observing how people rate the same reference points, researchers can estimate individual differences in scale use.

Cost-effectiveness estimates vary by an order of magnitude depending on how WELLBYs are valued relative to DALYs.

Open Philanthropy

Each scale point represents an equal welfare increment. If violated, summing is invalid and interventions targeting different baselines become incomparable.

nterpersonal Comparability LSA = 7 ≈ LSB = 7 implies UA ≈ UB When two people report the same score, they experience similar welfare. Scale-use heterogeneity violates this assumption.

equires four implicit assumptions

1 WELLBY = 1-point increase on a 0-10 life satisfaction scale × 1 person × 1 year W = Σi Σt LSit

We'll produce a practitioner-focused summary document, belief elicitation results with confidence intervals, and structured notes.

Participants can opt out of recording for specific segments if needed

(Note: QALYs may be more directly comparable than DALYs for this purpose.)

scale?

Where is the "neutral point" on the scale?

When comparing a mental health intervention (measured in WELLBYs) to a physical health intervention (measured in DALYs)

but more work is needed.

Each has strengths and limitations—and how they relate to each other, and whether either reliably captures what matters for human welfare, directly affects which interventions get prioritized.

adversarial manipulation.

Our results support AI as structured screening and decision support rather than full automation,

xhibiting consistent failure modes: compressed rating scales, uneven criterion coverage, and variable identification of expert-flagged concerns.

often approach the ceiling implied by human inter-rater variability on several criteria,

high-quality but noisy reference signal

narrative critiques

overing economics and social-science working papers

Peer review is strained, and AI tools generating referee-like feedback are already adopted by researchers and commercial services—yet field evidence on how reliably frontier LLMs can evaluate research remains scarce.

“high” reasoning effort

OpenAI Responses API

returned file id keyed by path, size, and modification time.

d the best performance from top reasoning models

Zhang and Abernethy (2025) propose deploying LLMs as quality checkers to surface critical problems instead of

but still recommend human oversight.

emphasize

The population of papers