ene-edited cell lines are the most under-modeled factor in published TEAs.
this is a strong claim ("most under-modeled") -- what's it based on? Reasoning transparency please. Provide support and links to this, tooltips etc. I want to make sure this is well-backed before I post and ~"co-sign" it !
technology and reaching different conclusions based on different priors about scale-up timelines and capital availability.
all claims need more direct supporting evidence ... quotes, links, etc.; tooltips are your friend
1. The $1–$100/kg spread is real disagreement, not just uncertainty. Named domain experts — Swartz ($25/kg) and Lattanzi ($100/kg) — are 4× apart with tight confidence intervals. This isn't a calibration problem; they're looking at the same technology and reaching different conclusions based on different priors about scale-up timelines and capital availability.
wait -- are you sharing the beliefs here? we didn't wnt to do that yet!
European Morning Drop-in Fri May 8, 2026 · 9:00–10:00am ET (3–4pm UK · 4–5pm CET) · Zoom Informal drop-in for EU/UK participants who could not stay for the full afternoon session. Primarily attended by European/UK participants (CET timezone). The session was a recorded Zoom — all attendees joined knowing this. It covered introductions and a preview of the hydrolysates and gene editing framing that would open S1. Full recording pending participant review before public release.
skip/remove this -- no one showed up
he session was recorded — all attendees joined knowing this
TMI -- put that in a tooltip, perhaps
David Manheim (Technion/ALTER) and Mirjam Capuder (University of Maribor) participated. The session was recorded — all attendees joined knowing this. It covered introductions, a walkthrough of the interactive cost model dashboard, and early framing questions about key modeling uncertainties. Full recording pending participant review before public release.
mention the insights here? I'm not sure we'll put out htis video either; it's not something interesting to watch , I guess
Context
italics
Direct quote (written submission)
these look like 'tick boxes' -- find a better way to depict this
Participants ▸ Show participant list (20 confirmed)
it already happened -- you don't need to say 'confirmed' -- doublecheck who came, and who had apologies, etc.
This workshop aimed to update expert beliefs using the latest evidenc
rephrase ... see language used elsewhere. We wanted to provide the opportunity for stakeholders, funders, and researchers to share evidence and update their beliefs in response.
while Believer Meats — the company behind the optimistic Pasitka TEA — closed in December 2025.
you keep pushing this -- it's ok to mention but the way it's written it seems like you are calling them out for hypocrisy; too harsh, and there can be other explanations
TEA researchers
are we sure about all these categories -- doublecheck this was the case. And maybe "TEA experts/forecasters" (a tooltip can explain what this means)
CM_01
hyperlink
Research and evaluation priorities — Which specific papers, issues, or research questions should The Unjournal prioritize for expert evaluation as part of the CM cost viability Pivotal Question? What new research (e.g., TEA reviews, production cost benchmarks, expert elicitation)92For context on what's already been identified: see the workshop reading list → and the UJ CM research scoping on Coda → or data would most reduce remaining uncertainty? [pre-session note]93David Manheim (Technion/ALTER, participated in May 6 pre-session) suggested we also consider: AI-powered and robotics-driven advances in manufacturing and bioengineering that may spill over from other fields, and scenarios where non-chicken species (salmon, veal) become the viable CM product first. [Suggested discussants]Suggested discussants:Matt McNulty (Tufts CCA) — strategic view of research priorities from an academic CM centerElliot Swartz (GFI) — has systematically mapped CM research gaps; knows what evidence would most shift the cost pictureJakub Kozlowski (model developer) — knows where the model's input uncertainties are largest; can specify what data would most tighten them
Eliot: Feed conversion ratio, taste studies on inclusion rates, actual cost of equipment, capital needs; scale out or up?
"We know more about media costs already"
What would commercially viable CM production actually require; on what horizon; and what does industry experience suggest about the factors beyond production cost that determine whether a CM business can sustain itself? Price parity63Parity could be with conventional meat, a hybrid product (cells + plant-based inputs), or a niche premium product. The relevant cost target differs significantly between these cases. is not the only route; hybrid products, niche markets, or differentiated positioning64Consumer acceptance, regulatory pathways, and market positioning are important questions that may be covered in other workshops or projects. But these routes to viability are relevant context for understanding what cost targets need to be reached. are also paths. Discussion space — unfold & annotate via Hypothes.isPaths to commercial viability — discussion spaceUse #question to flag something for verbal discussion during the session.Requirements for viability by 2036 [?]65What needs to be true — technically, financially, or politically — for CM to reach viability by 2036?Hybrid/niche paths [?]66Are hybrid products or niche markets a more realistic near-term path than full cost parity with conventional meat?Lessons from company challenges [?]67What do recent company difficulties teach us about what viability requires beyond production cost; including return on R&D investment?Other (S2 viability) — annotate here to add a point not covered aboveQuestions for discussants — annotate here to surface your question during the session[how this works]68Annotate this page via the Hypothes.is sidebar to leave a question or comment. Add #question to flag it for verbal discussion; #zoom for immediate Zoom chat attention. Reply to or +1 existing annotations; use @ to flag a specific person (who already commented on the page). Key uncertainties and research gaps [note]69These questions connect to S3: how should today's ground-truth from S2 shift our priors on CM's cost trajectory and AW funding value? [Suggested discussants]Suggested discussants:Matt McNulty (Tufts CCA) — strategy & operations at academic CM center; systematic view of where evidence is thinnestElliot Swartz (GFI) — wants to discuss modeling_hack + tea_review; GFI systematically identifies high-value research gaps
European space agency tender for CM in 2021!!
CDMO cost reality [?]59
20-25K per week for normal 200l bioprocesss -- JM ... maybe 15k with discounts. [Jordi's quick take]
S1 claims: push-back from practice [?]54Which S1 technical claims do you push back on from production experience; how cost-relevant is the gap?
An industry practitioner says: Focus on what is practical, where can I produce it, what will it cost
Cell line choic
Oana suggested that embryonic stem cells would have a consumer acceptance problem. I don't quite see why that would be (animal welfare wise, isn't it just a few cells here as the starter, not many calves)? #question ... would it otherwise be high-value
strategies
Example: immortalization strategy comment #zoom #question
Growth factors: the most uncertain cost driver
"most uncertain" is stated a little bit too strongly here. .. Perhaps "Arguably the most uncertain "
The solution is cell immortalization
Double check: is this the only solution, or are there other approaches?
Cultivated meat — also called cell-based or cultured meat — is produced by growing animal cells in a controlled environment rather than raising and slaughtering animals. The basic idea is simple: take a small sample of cells from an animal, give them the right conditions to grow, and you end up with genuine animal muscle tissue, produced without the animal. That’s the concept. The reality involves some sophisticated biology and engineering, and understanding it is essential for anyone thinking seriously about whether this technology can become commercially viable. This overview walks through the main steps of the production process and flags where costs enter the picture at each stage.
This is likely very oversimplified and presents concepts that are already well known to most workshop participants, but some may be less familiar with the full process.
What these numbers represent: Simulated manufacturing cost per kg of cultured chicken cell biomass (wet weight, at harvest ⓘ) in 2036, based on 30,000 Monte Carlo simulations. Wet-weight hydration assumed ~80% (range ~75
some of the tooltips are not coming up -- like here!!
--- ## Interactive Model ```{ojs} //| echo: false // ============================================================ // SEEDED RANDOM NUMBER GENERATOR // ============================================================ // Simple mulberry32 PRNG (fast, good quality for Monte Carlo) function mulberry32(seed) { return function() { let t = seed += 0x6D2B79F5; t = Math.imul(t ^ t >>> 15, t | 1); t ^= t + Math.imul(t ^ t >>> 7, t | 61); return ((t ^ t >>> 14) >>> 0) / 4294967296; }
Note, it's doing the sampling in straight javascript
Growth factors (GFs) signal cells to proliferate — at current research-grade prices, they can dominate media costs. The slider below sets P(at least one scalable production route — e.g., autocrine cell lines, plant-based farming, or precision fermentation — reaches commercial scale by the projection year), switching between “expensive” and “cheap” GF price regimes. Code viewof p_recfactors = Inputs.range([0.1, 0.9], { value: urlNum("p_recfactors", 0.5), step: 0.05, label: html`P(Scalable <abbr style="cursor:help;text-decoration:underline dotted;" title="Growth Factor — signaling proteins like FGF-2, IGF-1, TGF-β that tell cells to proliferate. Currently the most expensive media component.">Growth Factor (GF)</abbr> technology)` })
link to the learn/explainer page on the different types of potential GF innovation !
Parameters where source grounding is weakest (review priority):
Another folding block that should be folded by default.
Sources cited for context but NOT directly integrated into parameter values:
Also, this should probably be a folding block folded by default.
Sources that directly informed model structure and parameter ranges:
This probably should be a folding block folded by default.
::: {.callout-note collapse=“true”} ## Model change log — basal micronutrients folded into media (April 2026) Earlier versions of this model carried a separate food-grade micronutrient cost line (vitamins, minerals, trace elements) with its own adoption toggle, usage range (0.1–10 g/kg), and price range ($0.02–20/g). External review flagged two problems:
This callout block is not rendering.
See the About page for the full participant list.
You should be able to put an anchor on the section of that page "Confirmed Participants (RSVPs)" , And link that anchor here.
d production cost per kilogram of pure cultured chicken cells
not bold. Too much use of bold in general
See Vose (2008), Risk Analysis for a textbook treatment of correlated sampling in cost models, and Morgan & Henrion (1990), Uncertainty for foundational discussion of dependent uncertainties.
Make this a tool tip
copula-based correlation
Use less bold text. Italics are generally preferred
uptime
Up time as a share I assume
❌ Hydrolysates don’t help
"don't help" seems slightly too strong -- see previous notes #implement
Hydrolysates
Add as tooltip (condense a bit "What we think hydrolysates can do:
Replace the amino acid and peptide nutritional content that serum also provides (serum is ~60% albumin, plus amino acids, growth factors, hormones, lipids)
Possibly contain trace bioactive peptides that have modest stimulatory effects
Reduce the amount of growth factors needed by improving overall cell health and nutrition, so cells respond better to lower growth factor doses)
completely
replace with "basically" #implement
P(Scalable GF technology)
Give a tooltip here if possible "Growth Factor" - or just sell the word out the first time #implement
Code viewof reset_adoption = Inputs.button("Reset adoption defaults", { reduce: () => { // Set viewof values back to defaults viewof p_hydro.value = 0.75; viewof p_hydro.dispatchEvent(new Event("input", {bubbles: true})); viewof p_foodgrade.value = 0.65; viewof p_foodgrade.dispatchEvent(new Event("input", {bubbles: true})); viewof p_recfactors.value = 0.5; viewof p_recfactors.dispatchEvent(new Event("input", {bubbles: true})); viewof gf_progress.value = 50; viewof gf_progress.dispatchEvent(new Event("input", {bubbles: true})); } }) Reset adoption defaultsreset_adoption = 0 Code viewof p_hydro = Inputs.range([0.3, 0.95], { value: 0.75, step: 0.05, label: "P(Hydrolysates for basal media)" })
'reset adoption defaults' button is invisible -- too dark so too little contrast with the text.
Make reset defaults buttons more prominent throughout. #implement
Comparing LLM and human reviews of social science research using data from Unjournal.org CodeShow All CodeHide All CodeView Source
Important -- bring in/merge updated content from https://valentinklotzbuecher.github.io/llm-uj-research-eval/
Meanwhile, link to that one for "the latest research"
Table A.3 repeats the same structure using each model’s own maximum matched sample.
doublecheck that these are NOT within the model context window
Table
These numbers are being limited by Opus - we should do more evaluations in Opus and/or report the numbers just for the larger group.
ious Methods) ch
extra ")" -- remove
Table 3.4: Human-human vs Human-LLM agreement by criterion (Krippendorff’s α)
has this been adjusted for the fact that we're comparing the machine LLM ratings to the average of human ratings rather than to individual human ratings? If not, it's would be an unfair comparison relative to human-human (averages have less dispersion), and there's a specific way to adjust for that.
Table 3.3: Inter-rater agreement: Krippendorff’s alpha by criterion
Which comparison is this for? Is this human-human?
Pre-booking evaluator time before paper selection (Pacchiardi); paying more for flexibility.
This seems like a great idea to me. Talked about it in the past, as well as giving evaluators some limited choice over which ones they would like to evaluate.
Strong consensus against technical AI safety expansion.
I take this to heart, and I'm particularly concerned that we just might not have the right team to do this well. That said, I'm not convinced yet that the space is fully covered in terms of rapid, expert, credible evaluations of this research. The alignment journal, for example, is thinking of acting a bit more like a traditional journal, and it also is limited in domain, as far as I understand, to alignment research and not other aspects of technical AI safety research.
reviewable documents (model cards, technical reports)."
But peer review of model cards and technical reports would indeed bring us into a more "technical AI safety" sphere, wouldn't it? And people were generally against that, as you see in the next section.
Habermacher pushes for "politically grounded ('realpolitik' type)" regulatory frameworks.
This synthesis seems to be overly abbreviated. It's not clear what is meant by this. What sort of research are we talking about? What methods does it use? Who produces it? What ways could it be evaluated? How could it inform impactful decisions and Pivotal questions for particular funders and policymakers?
gaps in "regulatory interventions,
I need to see some examples of this work to see if it's something that fits easily with our general approach and skill set
estimates of risk parameters
This seems quantitative, which is more comfortable to us.
middle-power strategies and China cooperation lessons from arms control
I need to understand this work better. What is meant by this? What are some examples of such work? I am not sure if we want to extend ourselves to less quantitative/less empirical analysis (International Relations?) I'm also not against it, but it would be a bit of a move from our current approach and perhaps our comfort zone.
NBER-track pipelines)
How is NBER overlooked? I don't understand. Maybe a quote from Tagat here would be helpful
ugh Tagat warns "there is already a lot of ongoing work related to labor market impacts," suggesting the Unjournal should differentiate (e.g., evaluating funder-commissioned work, NBER-track pipelines).
I don't understand this. Why is it a "warning"? That would seem to be a good thing. We're not the ones doing the research; we're the ones prioritizing it, curating it, commissioning its evaluation, bringing feedback together, pushing it towards impact, etc.
AI × economics/labor markets
Very much in our wheelhouse, and there is strong work. But does this really get at the GCR-relevant issues / the issues with the highest global impact?
This requires interoperability standards across institutions, which is harder but achievable.
This will be much easier with the use of coding tools. Standardisation will be less important/detailed
Mandate open access for AI training. Require that all UKRI-funded research outputs be deposited in formats that can be used for AI training, and that this right cannot be contracted away to publishers. This does not require buying anything and it closes the loophole that allows compliance with open access mandates without genuine openness.
Concerns about AI alignment etc?
For quick inline notes on specific text or a parameter, use Hypothesis (click the < tab on the right edge). For anything beyond a brief highlight, prefer GitHub Discussions so the conversation stays organized and discoverable.
Hypothesis comments got lost, maybe in a page change because it changed the URL?
David van der Linden - Model validation Jacob Kozlowski - Financial modeling
David van der Linden - Model validation Jacob Kozlowski - Financial modeling too strong ... they are involved
grade CAPEX 5–25/kgcapacity|Basecapitalcostat20kTA|CAPEX_{}$ in scaling equation
Clean Up the LaTeX here; it's not rendering right. #implement
echnical Reference
Rename as "Model formulas" #implement
Media Cost=(1000density)⏟L per kg wet cells×turnover⏟media changes×price⏟$/L Variable definitions: Cell density (g/L): Final concentration of cells at harvest. Higher density = less media needed per kg. The 1000 converts g/L to L/kg (since 1 kg = 1000 g). Media turnover: How many times the media volume is replaced during a production run. Batch systems (turnover = 1) use one fill; perfusion systems (turnover = 3–10) continuously flow fresh media through. Price ($/L): Cost per liter of basal media (amino acids, glucose, vitamins, minerals — excludes growth factors, which are modeled as a separate cost component). Some literature sources report “complete medium” costs that include growth factors; our model separates these to allow independent uncertainty analysis.
the TEA comparisons give different costs per liter, but I suppose they also give different cell densities. Should we interpret these as independent, fairly uncorrelated variables, or is the cell density more or less scaling with the price per liter in a way that using the actual cost per liter makes less sense?
Session prep, agenda, follow-ups, and broader workshop-level discussion tied to the April 2026 Cultivated Meat PQ Workshop.
should be May
Media cost ($/L)
the TEA comparisons give different costs per liter, but I suppose they also give different cell densities. Should we interpret these as independent, fairly uncorrelated variables, or is the cell density more or less scaling with the price per liter in a way that using the actual cost per liter makes less sense?
Goodwin et al. 2024 (Nature
Add a link to this paper and to other papers within the table (a hyperlink) to save people time.
Does gene editing regulation affect growth factor costs?
Claude: Suggest moving this entire discussion to the workshop site. This is a workshop-specific topic about how to frame CM_01, not dashboard educational content.
Replace with a one-line link: 'Regulatory implications for growth factor costs and how they affect CM_01 framing are discussed at our workshop site.'
Same applies to the 'Which market?' section further down. Together these save ~40 visible lines and reduce cross-site duplication.
Claude: Content trimming plan for this page (1,147 lines → ~700 target)
Proposed changes in priority order: 1. Fold FBS/serum-free section — historical context, no longer reflects current practice. One visible sentence + collapsed detail. 2. Move jurisdiction & 'Which market?' discussions to workshop — these are workshop topics, not dashboard content. Replace with one-line links. 3. Fold GF signaling diagram — educational but not cost-relevant. Keep the price/solutions tables visible. 4. Bold audit — reduce bold to headings + key numbers only (addresses multiple reviewer comments). 5. Trim Further Resources — link to workshop resources page for full list. 6. CSS fixes — larger diagrams, less whitespace around SVGs.
Optional: fold 2-3 SVG diagrams (cell banking, seed train) to reduce scroll length — the text already explains these steps.
Full plan at .private/content_trimming_plan.md. Feedback welcome here or in chat.
1. Global Decision-Relevance (most important)
this should be specified somewhat by field
wo-track assessment Criteria weights depend on whether the work is prominent or not:
Also: Let users choose their own weights with sliders as well
5. Methodological Potential
is it a 'strong data set' etc
Animal w
How do we prefer stated preference here? I think revealed preference and real choices are more credible
Field-appropriate standards (don’t penalize fields where RCTs aren’t possible): Development/health: RCTs, DiD, regression discontinuity, IV Environmental/climate: Integrated assessment models, panel data, natural experiments AI governance: Mixed methods, surveys, formal models Animal welfare: Stated preference, DCEs, welfare calculations Political science: Quasi-experimental, panel data, surveys Macro/trade: DSGE, gravity equations, synthetic control
how literally is it using these 'standards' ? This bears some more expansion
NBER working paper,
caveat this if it's a conference paper
Show detailed scoring rubrics & methodology
Make this more prominent.
Key Claims to Evaluate
add a category -- 'methodological and theoretical/technical issues to be evaluated ... rate 'evaluability'
In an economy with manual labor, cognitive labor, physical capital, and AI, optimal tax policy can include taxing AI.
provide tooltip quotes to check for hallucination
What’s the chance that production costs fall below key price points
Not bold, avoid bold.
To explore these costs interactively, see our
not bold. Too much bold in general
optimistic
how is that 'optimistic?'
Note -- I want direct quotes from original sources (in tooltips) for important quant claims
Cells grow on scaffolds, media flows through
K: scaffolding may not be a cost-effective option, low cell densities
2024: PMC meta-analysis (Garrison et al.),
Where is this used? I mostly see the earlier sources mentioned above.
Source
The diagram is still too small. Make it bigger, please
all
not bold
Research Price Production Target Source
can you have columns with the cost/kg of output for each of these, at each of the prices
Cell densities have improved dramatically:
keep this prominent (out of fold)
The diagram compares these two operating modes side-by-side. Batch mode (left) harvests everything at once; perfusion (right) continuously adds fresh media and removes spent media while retaining cells.
too much white space before and after image
Batch vs. Perfusion: Two Operating Modes
give a tldr and the rest should be a folding box
underscores why most cultured meat companies currently use pharma-grade equipment despite the cost premium.
Is that still true?! Source please
The opportunity: If cultured meat can use simplified food-grade designs (similar to beer brewing at $5-15/L), costs could drop by 10×.
are they already doing this? If so, maybe adjust the wordking/emphasis here
Relative cost
give absolute cost/Kg too
2021
whose?
$10-15/kg, with some leading-edge companies achieving costs below $10/kg”
who is this quote form? "achieving" needs a reference
grade
at what cost/L?
of cells matters enormously for cost:
"enormously for cost' Seems potentially too strong here. ... as we have suggested above, the estimated cost share for cell banking is less than 1% of the total cost.
Am I correct? If so, please moderate this.
Step 1: Cell Banking
For each step, give a 'tldr' and an estimated cost share, and have the rest be something folded by default, which they can unfold
: <1% of total production cost at scale
references Humbird and Risner -- adapt htis to also reference work from GFI
Seed Train
missing bold label in diagram for this
Today, optimistic projections suggest ~$63/kg (Garrison et al. 2022), with leading companies achieving <$10/kg cell mass.
Flag a note (more discussion in tooltip) about how this is the cost of pure cell mass, and early/ultimate products might be hybrid CM, plant-based, fungal etc. ... so this overstates the cost, in a sense
"Achieving $10/kg" is probably too strong. Maybe 'claiming the ability'? And do you have a link to this?
Product
diagram below a bit small. Last item looks like a drumstick -- maybe make it look like a 'chicken hamburger' instead? (Because early products unlikely to have bones)
🔬 Workshop: Cultured Meat Cost Trajectories (Late April / Early May 2026)
make these folding boxes with extra content in tooltips
key levers
The high cell density is in blue, but you also put "micros" in blue, which suggests the two have a link. I don't think that's what it is. I think the high cell density will reduce the media cost, which is in green, and maybe other goals like bioreactor and operating expenses so I'm a bit confused.
Typical Cost Breakdown ($/kg chicken)
Diagram below does not really make sense. Is it a breakdown of the cost components or something having to do with levers that could make the costs go up or down substantially? This needs more clarity.
growth factors
Hyperlink is not working so well
How Cultured Chicken is Made Code
Top of this, or maybe on another page, it would be nice to have some sort of mosaic graph with different cost break-downs for different scenarios. Dividing up the cost into different components to get to total cost per kilogram, and then perhaps each of those mosaic elements could link to a different section explaining it.
This reasoning underlies our model’s binary switch approach —
This part of the model and also define what you mean by "binary switch" specifically in a tooltip.
insulin and transferrin
I don't think these were mentioned anywhere either - Dash. Are these growth factors? If they're not growth factors, why are you discussing them in this section?
albumin
What is albumin? You did not mention it anywhere else in this document. Is it a growth factor?
Solutions Being Developed
Which of the growth factors do these "solutions" pertain to?
Market data
Where did you find the market data ?
Current Price Target Price
It doesn't really matter what the price is per gram of inpuy . The question is, what is the likely price per kilogram of chicken meat output. add a column for this.
Most companies scaling up have already adopted hydrolysate-based media.
Tooltip example here, please.
most companies
Give listings, examples, and links/citations with tooltip quotes.
Key Growth Factors for Cultured Meat
Why are you telling me about all these different kinds of growth factors? Do they all need to be used? Are they alternatives to each other? Have you defined what the terms in the "function" column mean?
And how much of them will need to be used per kilogram of chicken meat produced (or whatever weight we are standardizing things to here), what cost implications? Right to always bring things to this standard unit of cost per kilogram of chicken meat.
Cost ($/L)
How does dollars per liter map into dollars per whatever unit of chicken meat we're using here? It's going to depend on the cellular density. I presume the cellular density is the same for these two types of media, or does one lead to much less dense cells?
Hydrolysates: The Big Win for Amino Acids
To what extent is it clear that these can just simply be used, and to what extent is this still an important uncertainty? If it's clear that they can be used, We should make that clear-- to flag this so people don't think of it as still an important uncertainty. But we should look for more references here to be sure.
The cultured meat industry must use serum-free media.
Try not to state things in a very prescriptive way. We're meant to be providing background information, not ordering people around.
This is THE Pivotal Uncertainty (click to expand)
Again, this really just seems too strong a statement to make. We need a little bit more epistemic modesty and reasoning transparency.
serum-free media d
Another hovering "?" here
Global FBS supply: ~500,000 L/year
Where exactly does it come from though?
expand
Maybe rewrite the headline to actually say that the total media cost is predicted to be out 40 to 70% of production cost, or whatever the numbers tell us. I don't need to expand it to get the headline result.
0-70% of total production cost (Humbird 2021, Risner et al. 2021).
Any way you could cite more recent references?
<5% if breakthrough technologies
Which ones give an explanation, maybe in a tooltip?
research scale
But why focus at research scale? Could you give what is projected to be a production scale?
Ethical: Derived from fetal calves — defeats purpose of avoiding animal slaughter Limited supply: ~500,000 L/year globally (van der Valk et al. 2018)
Where does the supply come from? Are animals being killed here to produce it?
This might be a folding box. I'm not sure if it enters into the previous narrative. ?
Traditional cell culture uses fetal bovine serum (FBS) — a complex mixture that provides growth factors, hormones, and attachment proteins. Problems:
So which of the above is this used for? It seems like it covers several of the above things you're calling "media". That's a little bit confusing to have this overlap of some sort.
grade (Sigma-Aldrich pricing)
Give me some excerpts from that page and explain what it means. You just linked to a sort of commercial page. It's not very helpful or easy to navigate.
Traditional cell culture uses fetal bovine serum (FBS)
A question mark comes up when I hover over this, but I don't see any tooltip explaining what it is.
Also, why are you talking about bovine serum if we're thinking about chicken here? At least you should explain the analogy.
Moderate
Quantify this.
Vitamins Metabolic cofactors B-complex, etc. Minerals/salts Osmotic balance, enzyme function
Maybe group the cheapest things together in one row unless there's some sort of environmental or ethical issue with them.
FGF
Tool tips should explain what each of these things are.
costs
Not all costs, just this component of cost. Again, I want to know what share that makes up of the total to put this in perspective. It's only a minor share of total cost. It's not really a pivotal cost driver, is it?
Try to put these in terms of cost per unit of meat produced in a mature production process, and try to use the same units everywhere so we know how to compare each element and sub-element.
Cell culture media contains everything cells need to grow:
List these by order of estimated share of cost in a production-scale process. And give a rough estimate of those shares, and those should be on the same scale - expressed per unit of output, in the same units. Give a disclaimer, of course, that this is just based on one particular estimate, and you can link to the actual model.
Hydrolysates vs. pure amino acids
What share of media costs are these in different models and reports? I thought this was possibly the largest?
media turnover parameter in our model
Link this part of the model. Backlinks might also be good from the model to this explanation (here and everywhere else. )
sio
Batch versus perfusion? You haven't given enough narrative here. I don't know why you're telling me this. Are these different bioreactor types, and if so, how does it map into the categories you just gave above?
Simplified designs for food production
This is a little confusing to me because what do you mean designed for food production? What is the standard food production use of this if not for cultured meat?
Batch vs. Perfusion
These images below are also too small.
Source
These sources are all from 2021 or earlier - can't you find some updated cost figures?
ou need far fewer reactor transfers
What's the typical cost of the reactor transfer in an established, larger-scale production process? Would this still be a substantial share of total costs?
less total media.
But isn't the sort of media that achieves higher density more expensive? I'm actually uncertain about this.
often pharma-grade at $5-20/L
Source for the quote "often pharma grade?" Okay, you're relying heavily on Humbird here. Find some other sources, and I've heard that now most companies are using food grade instead of pharma grade. Look into that and discuss in tooltip footnotes.
Cost Impact
For each phase, I want you to give some indication of the share of costs, in terms of the total cost per unit of meat, that this could potentially encompass, both at a small scale and at a larger scale.
Seed Train: Progressive Scale-Up Vial 1 mL 10⁶ cells T-Flask 100 mL 10⁷ cells Spinner 1 L 10⁸ cells Small Reactor 10 L 10⁹ cells Medium Reactor 100 L 10¹⁰ cells Production 1,000+ L 10¹¹+ cells
The text is a bit crowded here, so the numbers overlap the words. Try to adjust to give it a little more space.
Step 1: Cell Banking What Happens
Give more continuous references, perhaps as tooltips, to where you are getting this information from about the process. Perhaps give citations with links and short quotes.
require regulatory approval.
Link to this regulatory approval thing - how difficult/Costly is it to get that approval, or do we already have this for the important immortalized cell lines?
one-time setup cost that’s amortized over many production runs. A well-characterized cell bank can support years of production (GFI 2021).
Doesn't really explain how the costs work. Ultimately, the banked cells are used up, correct? Are you saying that cell banking is just a tiny share of the cost here, if you end up using the whole batch, is that right?
1. Biopsy
Image is still a bit too small.
2. Isolate Cells
Are we trying to isolate and why? Maybe this is a tool tip? And what's enzymatic digestion?
Step 1: Cell Banking
You did not use the term "cell banking" in the flow chart above. This can be confusing when you change terms. We don't know what Maps to what
Pasitka et al. 2022
Give the name of the paper and a tooltip, and also explain what aspects of these claims the source provides, perhaps with quick quotes.
Similar FGF-2/IGF-1 requirements to bovine (~10-100 ng/mL optimal)
Explain, perhaps in a tooltip, why the similarity is helpful here. That I don't really know what these things mean (e.g., what does ng mean?)
~70 billion chickens slaughtered annually vs ~300 million cattle
Provide a tooltip/link to discussion from animal welfare advocates about this, perhaps on the EA forum.
Kim et al. 2024
Tooltip hover should show the name of the paper, et cetera.
Produc
Can you make an image without a bone in it that still looks like a piece of chicken meat? I don't think bones are happening any time soon in cultured meat.
Seed Train
What's a seed train?
Step 5: Growth Factors — The Pivotal Challenge
Seems too strong. Media costs exceed GF costs in many formulations I've seen.
This is THE pivotal uncertainty. If any of these approaches succeeds at scale, growth factors become negligible (<$1/kg chicken). If none succeed, growth factors could be >$100/kg — making cultured meat uneconomic at scale. See GFI’s analysis for detailed technical roadmaps.
this seems a bit too strong from my reading. Media costs exceed GF costs in many formulations
Optimistic TEA ($6/lb) — useful comparison but model does not replicate their specific assumptions
I don't think this is for pure cell mass, this is for a hybrid product
Limitations
folding box
0 = nascent, 1 = mature
a continuous variable I guess, not binary. Make this clearer to avoid confusion
These are like Squiggle/Guesstimate visualizations - they show the full range of possible values, not just a point estimate.
Based on which parameters? the user-entered ones above?
Basic Parameters Code viewof plant_capacity = Inputs.range([5, 100], { value: 20, step: 5, label: "Plant Capacity (kTA/yr)" })
Important -- are these means or medians of a distribution used in simulation or are these simple 'degenerate' numbers. Explain and signpost better
And do they affect the figures and graphs below? #important
Individual distributions for each cost driver: Code function formatCost(val) { if (val >= 30) return Math.round(val).toString(); if (val >= 1) return val.toFixed(1); if (val >= 0.1) return val.toFixed(2); return val.toFixed(3); } { const allComponents = [ {name: "Media", data: results.cost_media, color: "#27ae60"}, {name: "Micronutrients", data: results.cost_comm_micros, color: "#3498db"}, {name: "Growth Factors", data: results.cost_recf, color: "#9b59b6"}, {name: "Other VOC", data: results.cost_other_var, color: "#7f8c8d"}, {name: "CAPEX (annualized)", data: results.cost_capex, color: "#e74c3c"}, {name: "Fixed OPEX", data: results.cost_fixed, color: "#f39c12"}, {name: "Downstream", data: results.cost_downstream, color: "#1abc9c"} ]; // Filter out components with all zeros (e.g., downstream when not included) const components = allComponents.filter(c => mean(c.data) > 0.001); const plotData = components.map(comp => { const p5 = quantile(comp.data, 0.05); const p50 = quantile(comp.data, 0.50); const p95 = quantile(comp.data, 0.95); const clipVal = Math.max(quantile(comp.data, 0.98), 0.1); const clipped = comp.data.filter(x => x <= clipVal && x >= 0); const plot = Plot.plot({ width: 420, height: 180, marginLeft: 45, marginBottom: 35, marginTop: 10, x: { label: "$/kg", domain: [0, clipVal * 1.1] }, y: { label: null, ticks: [] }, marks: [ Plot.rectY(clipped, Plot.binX({y: "count"}, {x: d => d, fill: comp.color, fillOpacity: 0.7})), Plot.ruleX([p5], {stroke: "black", strokeWidth: 1.5, strokeDasharray: "3,3"}), Plot.ruleX([p50], {stroke: "black", strokeWidth: 2}), Plot.ruleX([p95], {stroke: "black", strokeWidth: 1.5, strokeDasharray: "3,3"}) ] }); const label = `${comp.name}: $${formatCost(p50)} (90% CI: ${formatCost(p5)} – ${formatCost(p95)})`; return {plot, label}; }); return html`<div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 1rem; margin: 1rem 0;"> ${plotData.map(d => html`<div style="font-size: 0.9em;"> <div style="font-weight: normal; margin-bottom: 0.3rem; color: #333;">${d.label}</div> ${d.plot} </div>`)} </div>`; } formatCost = ƒ(val)
Do these adjust as I change the sliders above? I changed the 'plant capacity' and I did not see any change here. What's going on? #important
Sources
only these 3? Does the model incorporate other info?
The model reveals several important patterns:
which model? Does this adapt dynamically?
WACC Low (%)
give some tooltip explainers here
Cost Breakdown by Component (Total: $122.51/kg):where(.plot-d6a7b5) { --plot-background: white; display: block; height: auto; height: intrinsic; max-width: 100%; } :where(.plot-d6a7b5 text), :where(.plot-d6a7b5 tspan) { white-space: pre; }
make the graph below a bit bigger
Price-competitive with conventional chicken
Wait -- adjust this to consider/note the CM inclusion rate (%) and cost of plant-based or mycoprotein ingredients ($/kg)
Plant Capacity (kTA/yr)
Where did the slider range and starting value come from? Explain in a tooltip
edian Cost (p50)
Have a separate number for each of these boxes (slightly less prominent) for 'hybrid product cost/kg' ...
User should be able to input a 'CM inclusion rate (%) and cost of plant-based or mycoprotein ingredients ($/kg) as parameters', and this should do a simple auto adjustment.
First just a simple adjustment, and later we make this part of the simulation model.
Also allow user to switch this 'hybrid product' on/off (box below 'Include downstream processing'
Why it matters: If production costs reach ~$10/kg (comparable to conventional chicken), cultured meat could compete at scale. If costs remain >$50/kg, the technology may remain niche
Caveat/note here about cost of producing the pure cultivated chicken cells, vs cost of the product that will have some percentage of these cells mixed with other (plant, fungal, etc.) ingredients.
Include capital costs (CAPEX)
why wouldn't you include these?
Bounded ranges
give some specific examples of cases where this is used
Probabilities, fractions
Note that for ~switching parameters, the model samples both from the probabilities of a switch to a different regime (a different discrete state of the world, e.g., a new discovery) and then, in each simulation, uses this probability to select a particular state. (word this better, and be sure I'm correct here)
NoteWe Want Your Feedback!
no "!"
This model helps evaluators and forecasters:
But atm we donm't have confidence in this model. I's more about fixing ideas and giving people a sense of what sort of modeling we want (and surfacing doubts and disagreements on this) so that we can productively collaborate.
Ideally, we'd also have a page/interface where people could 'build their own models' and we compare them.
1. Total Unit Cost
This should probably be mirrored or linked a bit more on the man quote model page so people understand where it comes from.
New to this topic? How Cultured Chicken is Made | 🎧 Audio Review (22 min MP3)
Skip this last bit. Try to condense the content at the top a bit more for this page
🔬 Workshop: Cultured Meat Cost Trajectories (Late April / Early May 2026) This model feeds into The Unjournal’s upcoming expert workshop on CM production costs. Workshop details & signup →
Don't need this at the top of the technical reference page.
If the price of high-quality plant-based hamburgers fell by 10% everywhere, how would global chicken consumption change?
Explain why we targeted this 'cross category' substitution. We understand it's initially counter-intuitive. TLDR: Impossible Beef is a defined category with fairly clear pricing and high quality, but chicken consumption is more animal-welfare-relevant than beef. But we have variants of this that are more within-category. Put the TLDR in a tooltip and a longer explanation in a folding box.
Some more detailed explanation at https://forum.effectivealtruism.org/s/kazWBBYXm2Rvya3y2/p/3Eh8MbqLwFBsD7GK2#Why_focus_on_chicken_consumption_ and https://forum.effectivealtruism.org/s/kazWBBYXm2Rvya3y2/p/3Eh8MbqLwFBsD7GK2#Why_focus_on_Impossible_Beyond_Beef_
PBA_01 · Focal Question · Substitution Effect
I'd link the Metaculus question right next to each question here.
comment
Still add your annotations and let me know what you think about hypothesis as a format for collaborative discussion.
Decision RelevanceUnderstanding the nuances of poverty traps and 'trappedness' can inform development policies and interventions aimed at poverty alleviation. This paper could provide insights into where resources and policy changes would be most effective globally.
This feels a bit vague to me. Are there specific policies that would be affected?
Can reverse cross-population comparisons.
remember -- we are not focused on cross-population comparisons for this workshop. It's more about 'which interventions yield greater welfare', which would generally involve differences in difference, ideally across comparable populations (but not always)
δ = discount factor for future years
Where did the discount and time factor come from? Where did these definitional equations come from? I didn't think most emply estimated WELLBY measures considered multi-year collection or impact. And are they really discounting?
what most intervention comparisons need)
Cut this. I don't think it necessarily holds -- a lot of interventions impact mortality.
Add to footnote -- the 'incremental' WELLBYs may be captured by observing differences between comparable treated and untreated populations.
Accounting
it's not really 'accounting' -- these are conceptual
UK Government: Official guidance for policy appraisal
A link to this would be helpful. The "Green Book". (I wonder -- how impactful has this actually been on British policy?)
Neutral point estimation: What is the actual neutral point on the 0-10 scale for different populations? How stable is it across contexts?
I suspect we don't have any good measures of this? There's the Peasgood paper but I don't think that was in a LMIC and I'm not sure how much it has been vetted?
We may quote specific responses with attribution unless you request otherwise. If you prefer your responses remain anonymous,
Adjust this -- "If you prefer your response to remain anonymous, please use a pseudonym and try to use the same one consistently if you're providing multiple responses." If you are fine with internal recognition but don't want any public attribution, please let us know and share any other concerns in the field at the bottom.
How likely is it that the simple WELLBY measure (as defined above) is the best or near-best measure—yielding no less than 80% of the value of the best measure—for cross-intervention comparison in the focal context? (State your best calibrated probability.)
I'm considering adjusting this one to
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 for all interventions? Please give your central belief, and 90% credible intervals"
-- with a slider that goes from zero to one, and two other sliders that allow that allow you to specify the lower and upper bound of the 90% CIs.
emonstrates that small transformations can reverse published findings.
NotebookLM:
"they applied their methodology to nine prominent results from the happiness literature—including the Easterlin Paradox, the U-shape of happiness in age, the ranking of countries by happiness, and the effects of marriage and children—and showed that the standard conclusions in all nine areas could be reversed using monotonic (specifically lognormal) scale transformations. They argued that these reversing transformations were "plausible," claiming they were no more skewed than the U.S. wealth distribution
However, later work questions the plausibility of this. .
A systematic conceptual defense of treating wellbeing scales as cardinal and comparable. Argues that deviations from cardinality are small and not policy-relevant.
See https://www.econstor.eu/bitstream/10419/232657/1/dp13905.pdf
Start here (30-60 min total)
It will take more than 60 minutes, but a skim might be doable in 60 minutes.
Confirmed Participants (15) — click to expand
Add "(rsvps)" -- Also update this, I think we have over 20 by now.
Note: human means carry their own variance; correlations here are bounded by human inter-rater noise.
is this ggplotly? Shouldn't it be dynamic? I don't seem to be able to adjust it
Table 3.1: Token usage and estimated cost per model
These are probably not updated costs -- Update and implement
Alberto Prati may contribute via pre-recorded video.
Not 'video', possibly some written content, or we can extract issues from his evaluation to ask Benjamin et al.
leads to least regret?
The "least regret" is a formal term in information theory, I believe, or from Bayesian updating. Provide a footnote defining and referencing it. #Implement
Annotate & Comment:
We'd especially like pre-session feedback on