We Use AI. We Are Not Building Slop.

Nobody Asked for More AI

Earlier this year the American Dialect Society picked its word of the year for 2025. It chose slop: low quality, high volume content, usually produced by a machine. [1]

That vote was not an isolated mood. Pew surveyed 3,488 US adults in late June 2026 and found that 52 percent are now more concerned than excited about the growing use of AI in daily life, up from 37 percent in 2021. Only 9 percent are more excited than concerned. For the first time, a majority of adults under 30 landed in the concerned group. [2]

The feeling gets much stronger when AI shows up as a physical neighbor. Gallup asked Americans in March 2026 whether they would favor an AI data center being built in their local area. Seventy-one percent opposed it, including 48 percent who opposed it strongly. For comparison, 53 percent oppose a new nuclear power plant nearby. [3] The Annenberg Public Policy Center watched local opposition to data centers climb from 49 percent to 61 percent in the space of four months. [4]

There is even a measurable penalty for saying the words out loud. In experiments with more than 1,000 US adults, adding "artificial intelligence" to an otherwise identical product description lowered people's intention to buy it. The effect ran through emotional trust, and it was strongest for high risk categories, the kind where a failure costs you money or health. [5]

Read that last study as a founder and the commercially optimal move is obvious. Say less about AI. Put something warmer on the homepage.

I am not going to do that. But I do want to be precise about what we are doing, because I think the skepticism is mostly earned, and I would rather explain myself than get quietly filed under the thing people are tired of.

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The Skepticism Is Earned

The complaint underneath the word slop is not really about machines. It is about accountability.

A confident paragraph that nobody wrote, nobody checked, and nobody can be held to is worth very little, no matter how good it sounds. And language models are extremely good at producing exactly that. When researchers asked chatbots to supply references for systematic reviews, 28.6 percent of the references from GPT-4 and 39.6 percent from GPT-3.5 turned out to be hallucinated, and 91.4 percent from Bard. [6]

Models have improved a lot since that study. That is not the point. The point is structural: text that has the shape of a citation is not a citation, and a system whose only output is fluent prose gives you no way to tell the difference.

About half of Americans told Pew they expect AI to make people worse at thinking creatively and worse at forming meaningful relationships. [7] I do not think that worry is silly either.

So when someone sees "AI" attached to health information and their first reaction is a flinch, I read that as a healthy immune response. Health is exactly where an unverifiable confident paragraph does the most damage.

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What We Are Not Doing

We are not training a model on nutrition and letting it talk.

That is the thing people are right to be tired of, and it is not what AllNutrition is. There is no model here that has absorbed a lot of nutrition text and now produces plausible nutrition opinions. If that were the product, the criticism would land, and I would deserve it.

The actual design goal is close to the opposite. Every claim we show is supposed to resolve back to a specific published paper, written by named humans, reviewed by other humans, with its own methods section, its own reference list, and its own funding disclosure. The library is tens of thousands of peer-reviewed studies, plus a small set of the most reputable clinical guidelines. If a claim cannot be traced to one of them, it should not be on the screen.

Peer review is a flawed institution. Reviewers work for free, fit the work into schedules that are already full, and often review papers slightly outside their exact specialty. One estimate put the global volume of unpaid reviewer time at over 100 million hours in a single year, with the US share alone worth more than $1.5 billion. [8] It is a system running on donated labor.

It is also, still, the best mechanism humanity has built for separating what is known from what someone wishes were true. I would rather build on top of an imperfect system with real accountability than replace it with a fluent machine that has none.

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But Why Not Just Follow the Experts?

This is the part I want to be careful about, because I am not anti-expert. Some of the clearest nutrition explanations I have ever read came from doctors, dietitians, and researchers who built large audiences by being genuinely good at this.

The problem is not that experts are untrustworthy. It is that expertise is narrow by construction, and every human being makes mistakes.

Deep expertise can even make it worse. Someone who has spent twenty years studying muscle and its relationship to longevity will read every new question through that lens. It is an excellent lens, and it is not the only one. That same person may not weigh your kidney function, your medications, your lipid trajectory, your family history, or a body of evidence sitting one field over that points somewhere else entirely. That is not a character flaw. No individual can hold all of this at once.

And the underlying literature is noisy enough to fool careful people. In one of my favorite papers, two researchers picked fifty ingredients at random from a cookbook and went looking for cancer studies on each. Forty of the fifty had at least one. Of the studies they found, 39 percent concluded the ingredient raised cancer risk and 33 percent concluded it lowered it. [9]

A credentialed, honest expert can build a confident story out of that literature. So can the equally credentialed, equally honest expert who believes the opposite. Both of them will cite real papers.

The way out is not a better expert. It is reading far more of the evidence than any one person can, and being explicit about where it agrees, where it conflicts, and how much each piece deserves to be weighted.

Which brings me to the number that made me start building this.

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Nobody Can Read This

I pulled the counts myself rather than trusting a summary, using PubMed's public API. Two deliberately different definitions, both plain keyword searches so that nothing depends on how quickly indexers catch up.

Papers with nutrition, diet, or nutrient in the title, meaning work that is directly about nutrition: 24,895 published in 2025. That is about 479 per week.

Papers using those words anywhere in the title or abstract, meaning work where nutrition is at least part of the story: 90,458 in 2025. About 1,740 per week, or roughly 250 every single day, weekends included. That second group contains the first one, so think of it as a narrow circle sitting inside a wide one.

Ten years earlier those numbers were 12,774 and 43,864. The literature has roughly doubled in a decade, and it grew faster than PubMed as a whole.

Here is the part I find hardest to argue with. Take the directly relevant papers, the ones with nutrition in the title, and add up everything published between 2000 and 2023. Almost a third of that entire 24 year total was published in the last four years of it. That is not a field you can catch up on by reading harder.

If both lines simply continue at the rate they have held since 2015, then by 2030 we are looking at roughly 670 directly relevant papers per week and about 2,500 per week that touch nutrition somewhere. Over 2026 through 2030 that is on the order of 150,000 papers directly about nutrition, sitting inside a wider pool of more than half a million that touch it.

I want to be honest about what that projection is. It is a trend line, not a forecast with error bars, and keyword searches both overcount and undercount. A paper that says "diet" once in an abstract about something else is in my broader number. A paper about time restricted eating that never uses any of my six words is in neither. The precision is not the point. The order of magnitude is.

And the growth is not evenly spread.

Nutrition crossed with ageing and longevity grew 3.4 times in ten years, which is roughly the exact question most people actually care about: not what to eat this week, but what makes a longer and healthier life more likely.

The other one is worth sitting with. Systematic reviews and meta-analyses grew 4 times. The layer of research whose entire job is to summarize other research is now growing about twice as fast as the research it summarizes, and those summaries routinely disagree with each other. Even the summaries need summarizing.

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And It Is About to Get Faster

Scientists use AI too. Not as a curiosity, as a daily tool.

A 2025 analysis in Science Advances looked at vocabulary patterns across more than 15 million PubMed abstracts and concluded that at least 13.5 percent of 2024 abstracts were written with LLM assistance, reaching 40 percent in some subfields. [10] Reviewers are doing it as well: an analysis of peer reviews submitted to several AI conferences estimated that between 6.5 and 16.9 percent of the review text could have been substantially modified by a model. [11]

Draw the obvious conclusion. Writing a paper is getting faster. Writing a review of a paper is getting faster. Running an analysis on an existing dataset is getting much faster. Meanwhile the trials that take years still take years, and the number of humans available to scrutinize each individual paper has not moved.

Volume goes up. Attention per paper goes down. Retractions passed 10,000 in a single year for the first time in 2023, driven largely by sham papers and compromised peer review. [12]

So the coming decade gives us more evidence and less scrutiny per piece of it. That is precisely the situation where careful, consistent, tireless triage is worth the most. And that is not a job a person can do, or a team, or a team that would then have to keep doing it every week forever without getting bored or developing a favorite theory.

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What We Actually Use AI For

Not for having opinions. For reading.

Retrieval, not recall. Answers are assembled from papers pulled out of the library at the moment you ask, not from what a model happens to remember. Every claim carries the sources it came from, and you can open them.

Appraisal, one paper at a time. Agents read the full text of each paper and score it on four things: methodology, conflict of interest, reporting transparency, and evidence strength. Those scores are visible to you, not buried.

Following the money. For each paper we look at the authors' publication history, their institutions, their disclosures, and who funded the work. Industry funding on its own is not a conflict. The thing that matters is funding combined with findings that happen to serve the funder, which is why disclosed industry ties that produced results against the funder's interest score well rather than badly.

Ranking, not deciding. Weaker or conflicted evidence gets weighted down rather than silently deleted, because "the industry funded trials say X and the independent ones say Y" is often the single most useful thing you can know about a question.

None of that requires the AI to be smart about nutrition. It requires it to be relentless about reading, sorting, cross referencing, and flagging, at a scale and a cadence no human team can sustain. The knowledge stays human. The machine is doing the part humans cannot: keeping up.

I wrote a while back that AI is not the product, and that has not changed. The mission is helping people make better decisions about food and longevity. AI is the most powerful tool currently available for that job, and if something better shows up, we will use that instead.

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The Test I Would Ask You to Apply

Slop is not defined by whether a machine touched it. Plenty of slop is written by people, and some of it gets published in journals. Slop is content that cannot be traced and that nobody is accountable for.

So the test is simple, and it works on us as well as on anyone else making claims about your health.

Follow the claim. Can you get to a named paper, in a named journal, with named authors, a named funder, and a methods section you or your doctor can argue with? If yes, you are looking at evidence, whatever tool helped surface it. If not, you are looking at slop, no matter who said it or how many followers they have.

You are completely entitled to dislike AI. I would just ask that you judge what we build by whether the claim tracks back to a paper.

Because the alternative is not a world without AI in nutrition. It is a world where 90,000 papers a year get published, almost nobody reads them, and the confident voice with the best distribution wins by default.

That world already exists. It is the one that produced the advice you are currently trying to make sense of.

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Sources

1. American Dialect Society. 2025 Word of the Year Is "Slop." January 2026. americandialect.org

2. Pew Research Center. Young adults in the U.S. are increasingly wary of AI, concerned it will take jobs. August 18, 2026. Survey of 3,488 US adults, June 22–28, 2026. pewresearch.org

3. Gallup. Americans Oppose AI Data Centers in Their Area. Survey of 1,000 US adults, March 2–18, 2026. news.gallup.com

4. Annenberg Public Policy Center, University of Pennsylvania. Opposition to Local Data Centers Rises Sharply, Annenberg Survey Finds. Survey of 1,320 US adults, June 16–July 19, 2026. annenbergpublicpolicycenter.org

5. Cicek M, Gursoy D, Lu L. Adverse impacts of revealing the presence of "Artificial Intelligence (AI)" technology in product and service descriptions on purchase intentions. Journal of Hospitality Marketing & Management. 2025;34(1):1–23. doi.org/10.1080/19368623.2024.2368040

6. Chelli M, Descamps J, Lavoué V, et al. Hallucination Rates and Reference Accuracy of ChatGPT and Bard for Systematic Reviews: Comparative Analysis. Journal of Medical Internet Research. 2024;26:e53164. doi.org/10.2196/53164

7. Pew Research Center. What the data says about Americans' views of artificial intelligence. March 12, 2026. pewresearch.org

8. Aczel B, Szaszi B, Holcombe AO. A billion-dollar donation: estimating the cost of researchers' time spent on peer review. Research Integrity and Peer Review. 2021;6:14. doi.org/10.1186/s41073-021-00118-2

9. Schoenfeld JD, Ioannidis JPA. Is everything we eat associated with cancer? A systematic cookbook review. American Journal of Clinical Nutrition. 2013;97(1):127–134. doi.org/10.3945/ajcn.112.047142

10. Kobak D, González-Márquez R, Horvát EÁ, Lause J. Delving into LLM-assisted writing in biomedical publications through excess vocabulary. Science Advances. 2025;11(27). doi.org/10.1126/sciadv.adt3813

11. Liang W, Zhang Y, Wu Z, et al. Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews. Proceedings of the 41st International Conference on Machine Learning (ICML). 2024. arxiv.org/abs/2403.07183

12. Van Noorden R. More than 10,000 research papers were retracted in 2023, a new record. Nature. 2023;624:479–481. doi.org/10.1038/d41586-023-03974-8

13. Publication counts in the two charts above were retrieved from the PubMed E-utilities API on August 26, 2026, by publication year. pubmed.ncbi.nlm.nih.gov