public negative
'the possibility of'
public negative
'the possibility of'
Candidate themes from the notes
Thank Ziyi Wang for their substantial work on this
Stanford Law Review, are edited by law students
this reference is not relevant -- it is focusing on European journals not US!
Legal scholar lead Candidate curation Law and AI partner Animal welfare law Pilot papers Paid labeling Evaluator pool Workshop route
what are these buttons meant to do? Are they supposed to be links?
How should the model differ between US law reviews and European peer-reviewed legal scholarship?
Or should we focus mainly on the US context because of the greater 'review gap' as well as the greater role for court jurisprudence in the US. On the other hand US legal scholars may be paid more, overcommitted to lucrative and influential work, and thus less willing to do the evaluations.
useful to legal audiences.
--> are highly credible to legal audiences, useful to practitioners, and show potential for global impact.
the project should restart only if it has legal-scholarship ownership and a narrow pilot.
reword this. A 'narrow pilot' is what we will do, that's not a precondition
Not a generic policy-commentary outlet; the focus should be assessable legal scholarship.
this is a bit vague, needs clarification
Not a replacement for law reviews or journals.
why not?
Identify public legal research with unusually high expected value for evaluation.
This itself would be a useful public good, if we curated it well, with feedback from organizations that wanted to use this.
Naturally we will do this in a human-AI collaboration, with AI doing much of the initial search and filtering. (see https://uj-prioritization-prototype.netlify.app/ for a prototype for our main stream)
Choose a narrow pilot
We did this about 8 months ago, but most of these will probably be stale
how quickly alternatives to animal-source foods must diffuse for the food system to make a meaningful contribution to climate targets. That is directly relevant to public R&D, procurement, regulation, investment, and philanthropic choices being made by organizations working on climate mitigation, food systems, and animal welfare.
relevant yes. But how do we know it's important for these questions?
Default σ (no CI)
Fix the tooltip -- it shouldn't be in all caps
PQ3C — P(HLI abandons WELLBY within 3 years)
give the full question language, and hyperlink the question in context (tooltip if long)
Respondent H
just say 'anonymous 2' .... "H" is confusing
PQ1A: What is your probability that linear WELLBY comparisons are reliable enough for comparing interventions in LMICs? Respondents gave a central estimate (0–100%) and a 90% credible interval.
Note -- I did not intent to have CIs over probabilities. This was an artifact of a changed question and vibe coding. Also investigate whether this was the wording of the question when participants answered it
Subquestions Timeline
these are subquestions, but we're missing some of the 'goal oriented' questions here
CM Workshop ·
add hyperlinks/headers back to the workshop here
Germany consumer survey · late 2024 Free GFI Europe consumer survey (late 2024, published 2025): 25% of German adults and 23% of UK adults reported consuming plant-based meat in the last month. 47% of German adults and 41% of UK adults reported already reducing their meat intake or following a meatless diet. 60% in Germany and 56% in the UK reported at least monthly consumption of some plant-based product category (broader than meat). Since only ~5% of German consumers exclusively consume alternative proteins (see src-35), the large majority of the 25% monthly PBM consumers are omnivores. Survey-reported personal consumption is more direct evidence of self-eating than purchase-panel data, which tracks household-level transactions without identifying wh
this seems to need more digging into!
Together: PBM is roughly 0.1–0.15% of conventional by volume, or 0.16–0.4% by illustrative retail valu
this seems worth highlighting, even if it's a rough calculation
~$7–9/lb vs ~$5–7/lb
state this as percentage
Workshop
Pre-worskhop discussion !!
In brief
there is too much overlap between this and the paragraph beginning "The sceptical concerns"!
Readings & Resources
We need more of a TOC and sections/navigation on this page. Longer content in folding boxes
Background note: a first-pass Claude summary of evidence on PBA penetration and taste-comparability is available for sharing. It is exploratory rather than a vetted literature review.
shorten this a bit
takes a broader view than PBA alone
Rewrite to "extends Beyond pba"
If a 10% price reduction in Impossible Burgers leads many people to eat fewer chickens, that suggests a case
This is a "for example"
PBA buyers were already eating less meat
"allready eating less meat" is. Vague. And. I'm not clear what the argument is here
plant-based burgers are mostly substituting away from beef (not chicken),
The lower animal welfare burden of beef vs chicken may not be known to all readers
welfare
replace: "corporate animal welfare campaigns"
Connect to decisions: Given current evidence, is PBA funding plausibly competitive with corporate campaigns?
Also mention other questions, such as "will meat taxes improve or worsen animal welfare?" and "Will innovative products such as PBA and cultured meat substitute for farmed animal consumption, or will they mainly be taken up by (existing) vegans and vegetarians"
Quantify uncertainty: What's a reasonable range for the cross-price elasticity between PBAs and chicken, given what we know and don't know?
This is kind of captured above, but I would do something more here with belief elicitation, interactive updating, and aggregating knowledge.
Broaden the scope:
This isn't something we're trying to achieve. This is just a path we're thinking of taking with the workshop.
and
and/or
s — p
Colon instead of dash.
nd can we conclude anything at all with current methods?
Rather than "conclude" something like "do currently available methods and data even yield useful insight?"
can we actually conclude about substitution effect
Conclude is too strong here. I would say, what can we reasonably say about substitution effects and with what confidence?
agricultural economists
Also industrial organization, quantitative demand estimation, economists, and quantitative marketing economists
brings together
Aims to - we don't have a confirmed guest list yet.
funders
funders and industry and charity practitioners
animal welfare researchers
Economists interested in animal welfare
, but stated choices may not reflect real purchasing behavior.
Tool tip with some reference discussion of the limitations here.
identification strategies vary considerably in rigor.
Mention the use of instrumental variables and other strategies here, perhaps in a tooltip. Give specific references in that tooltip.
raising questions about which to trust.
Add a tooltip here, discussing some of the strengths and limitations of each, using the context and explanations discussed elsewhere . Let me know if you need more context on this.
Different specifications can yield very different elasticity estimates.
... (tooltip) Note this is in part due to the aforemationed point that elasticity is not likely to be constant across an individual or market demand curve, and there will also be heterogeneity thus, it matters what parts of the curve you are looking at, and which markets, times, etc.
IV and experimental estimates often diverge in opposite directions from naive OLS.
rephrase this -- it's not quite right, and confusing
Also be clear: these are estimates of own price elasticity, although it seems unlikely that cross-price elasticities would be more consistent or robust. And these are price-shifting field experiments. But also note, in a tooltip, some of the critiques of these experiments themselves. Ask me if you need context.
especially in the earlier years when these products were emerging.
I don't see what this part of the sentence adds. If the data is available in later years, we can focus on that later data. Maybe just leave this out, or mention something like "partly because of the limited availability of these products, and lags in releasing data for research use." -- But That's tooltip details. Also, I want you to ground some of these statements with references and links, mainly in tooltips.
they anticipate lower demand,
More when they expect demand to be more price sensitive --- have pro or counter-cyclical pricing; Put the details in a tooltip
Why this is hard to measure
These explanations are taking up too much space and will take up even more when you consider a wider range of approaches.
Use folding boxes and tooltips more.
everal key challenges complicate this:
These are key issues with ~traditional econometric (IO and quant. marketing) methods.
Field experiments (supermarket-level or at school cafeterias etc.) have less of an endogeneity issue, but some of these issues are still present (e.g., short term vs long term), and these are hard to implement at scale and cleanly, and have issues of their own (see the notes/discussion, and sketch these).
Hypothetical and small-value choice experiments and hypothetical discrete choice surveys have other important limitations (mention these, from the sources and discussion).
that's a
That suggests
the strongest causal evidence.
moderate this. This is vague. and there are a few kinds of field experiments in addition to this, including price shift experiments (esp. Bray et al), although few if any involving PBA
though
not "though"
These measurement challenges mean we should interpret existing estimates cautiously, while still extracting what information we can. The workshop will discuss which methods are most trustworthy and what further research could help.
this is a bit generic, maybe not necessary
likely
likely --> "may" ... tooltip some other possible explanations for this. (discussed in the notes).
One concrete finding worth engaging: The evidence suggests that the vast majority of PBA purchasers are omnivores, not vegetarians or vegans — one study finds that only around 1% of high-spending plant-based meat alternative households are actually vegetarian. This challenges the intuition that "PBA just captures existing vegans" and raises the stakes for substitution estimation: the counterfactual meat consumption displaced may be much larger than assumed.
This is probly too strong ... needs caveating and referencing and tooltips.
have serious limitations that are worth confronting directly.
just "seem to have serious limitations, and existing estimates are 'all over the map'" -- hyperlink or tooltip and link https://forum.effectivealtruism.org/posts/3Eh8MbqLwFBsD7GK2/how-much-do-plant-based-products-substitute-for-animal#Existing_Research
Scanner dat
--> Observational demand estimation (using scanner, retail, and macro data)
And we're asking a prior question
rephrase. More like an "revisiting an underlying question"
(chicken vs. beef vs. pork),
make this 'between different animal products' and 'e.g., chicken vs. beef vs. eggs...' -- relevant for AW when considering issues like the AW impact of meat taxes -- which might shift consumption from beef to chicken, with a higher AW burden -- mention this briefly with further details in a tooltip
Germany PB meat substitute production volume, 2025 (Destatis)¹⁸
Skip this one
US plant-based beef price premium vs conventional beef, category average
Research and state this. Also for impossible and. Beyond vs conventionally ground beef
burgers
The correct term is hamburgers
Penetration
Penetration might not be the right term. I think we just mean market share here
Breaded centre-plate
Whatts. Breaded. Center plate?
Butcher) and the Nordic countries, where per-capita consumption of plant-based foods is high — probably sit above Germany, whic
Evidence for this claim? Otw State as ,,we. SpeculTE that,,
Claude) prompts;
Claude and various openai models
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. It was mostly preparation and broad discussion.
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
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
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
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.
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.
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?