Can AI Give You Good Nutrition Advice?
I tend to be pretty skeptical of AI. Not only does most AI writing sound like bad Rupi Kaur mashed up with every bit of writing ever on LinkedIn, but there are problems baked into the different LLMs (solipsism, hallucinations) that we're just beginning to see the downstream impacts of. Still, I wanted to check my priors (IMO, the best-case AI scenario is an inbox assistant you get for the low, low price of your planet's health, human dignity, and cognition. Neat!) and ask a real question: is AI nutrition advice any good? Can a chatbot help solve any problems, or fill any gaps, for people struggling with food and fueling?
Despite my personal distaste for our culture's propensity to shoehorn chatbots into every available corner of life (I do not need an AI shopping companion, Kroger!), I went to the evidence to see what we know, and what we don't, about how people are using (and potentially abusing) this technology when it comes to food.
The reality is that AI is a somewhat competent reference librarian and a terrible coach, with a darker side that research is just now revealing. It is strong at the thing nutrition mostly is not about, which is de-contextualized but abundant information, and weak at the thing nutrition mostly is about, which is your actual body doing actual things on an actual Tuesday.
AI did not escape diet culture. It's been metabolizing it. In addition to all the great stuff on PubMed, these models were trained on every SkinnyTok sh*ttake and Cosmo-coded millennial diet-advice column, and they don't weigh the former as heavily as you might think. They learned from the open internet (minus the paywalled stuff, which tends to be the best, because journalism costs money), which means the marketing of a multibillion-dollar diet industry sits in the training data right next to the peer-reviewed science, and the model cannot always tell you which is which. Especially if you, through repeated use, have primed it to pay more attention to one than the other. So before you let a chatbot plan your fueling or a photo app referee your lunch, it is worth knowing where the research says these tools help, where they fall apart, and where they might actually be doing harm.
What happens when you ask ChatGPT (or Claude) for a fueling plan
Last year, training for the Run Rabbit 100, I asked Claude to build my fueling plan. I gave it everything: fluid loss rate, sodium loss rate, elevation, predicted temperature, the exact specs of my gels and my potatoes, how I wanted to split it all across two or three flasks. Real inputs.
It took almost two hours of babysitting what is now, more or less, a U.S. government security contractor to get anything that obeyed the laws of physics. It kept recommending a 22-ounce flask, which is not a size that exists and would not fit in a run vest. It offered to remove the sodium from store-bought instant mashed potatoes, which is also not a thing you can do. It produced something that on the surface looked like a fueling plan, but that anyone without a traumatic brain injury would immediately recognize as laughably wrong.
I sent the whole mess to Kylee. She replied "lol" and rewrote it in about seven minutes.
A fueling plan for a 100 is not that complicated, and the stakes here were genuinely low. But a machine that can't wrap its "head" around how a potato works, a technology we've had since roughly the dawn of agriculture, is not the tool I want anywhere near my race-day calories.
Kylee, in her infinite wisdom, gave me usable advice in about one-tenth of the time. She balanced the data-dense side of the job with the part that actually matters: the human attached to the stomach. Nutrition runs on two things at once: the body, and the hard constraints of reality. Like how potatoes behave and what size flasks come in.
And even if Claude had handed me a flawless plan, I'm not sure I would have believed in it enough to follow it. A plan you don't trust is a plan you abandon at the first sight of flat Coke and sun-baked watermelon.
Where AI nutrition advice actually helps
Credit where it is due. On a validated general nutrition knowledge test, the major chatbots scored about 77 out of 88, which put them on par with dietetics students and well ahead of the general population (Bragazzi et al., 2025). In another evaluation, ChatGPT cleared 84 percent on a licensing-style nutrition exam (honestly, for something with access to THE ENTIRE INTERNET, that feels low??). As a textbook that talks back, it is genuinely useful. Ask it what a complete protein is, or to explain glycogen depletion, and you will get a clear, mostly correct answer faster than you would get one from a search engine.
The other real win is not knowledge at all. It is adherence. One systematic review found that AI dietary chatbots improved adherence to nutrition plans by about 32 percent compared to conventional counseling, largely because the bot offers continuous, in-the-moment feedback.
The thing that worked was not the AI's intelligence. It was that the AI answered at 9 p.m. when a human dietitian was asleep. (Here, I'd argue that the fact that dietitians are human and also need sleep and food is a feature, not a bug, but more on that later.) The most recent comprehensive review lands exactly where a sane person would put it: AI should augment dietitians, not replace them (good news, Kylee!), and it is most mature in image-based assessment and coaching for weight, diabetes, and cardiometabolic risk.
Great. So why aren't we actually any healthier?
Is AI calorie counting accurate?
Photo-based calorie counting, which is what most people actually mean when they say AI nutrition, is shakier than the marketing implies. Single foods like an apple or a slice of bread get recognized with 85 to 95 percent accuracy. I hate to brag, but I guarantee I'm more than 85 percent accurate at identifying an apple. Mixed dishes push the error rate to 30 to 40 percent, and portion estimation is where most of the damage happens. When a team of registered dietitians tested seven popular apps against more than a hundred real meals, accuracy ranged from 50 to 82 percent, and old-fashioned manual entry still beat the AI by a wide margin. The tool that promised to remove the friction of logging turned out to be less accurate than the logging it replaced.
Listen. A 50-to-82 percent accuracy range is the difference between passing your driver's license road test and getting pulled out of a flaming sedan in a ditch. Same test. Wildly different afternoon.
These LLMs are only as good as the data they're trained on, and as we've mentioned on nearly every episode of Your Diet Sucks, the data is not as good as you'd want it to be, especially for sex-specific or sport-specific questions. When chatbots were tested specifically on sports nutrition questions, accuracy ran from 74 percent on the best model down to 31 percent on the worst (Solomon et al., 2025). Big yikes.
For fueling decisions that actually matter, that is worse than a coin flip.
Then there are hallucinations and fabrications. Chatbots sometimes fabricate clinical studies that do not exist in order to support an answer (Yager, 2026). Check that citation. It exists because I'm a human, and I know how to Google sh*t. Y'all: always, always, ALWAYS check the sources (looking at you, RFK Jr.).
A tool that invents its own evidence is uniquely poisonous for nutrition, because AIs are incentivized to keep you talking. Instead of pushing back, questioning your premise, or saying you're wrong, they will say whatever they need to say to keep you chatting. Including inventing studies and journals.
AI won't tell you your question is wrong
This is the part that gets the least attention and deserves the most. AI is built to agree with you. Researchers call it sycophancy, and it is one of the things that is both scariest to me and the most annoying. A 2026 study in Science measured this across eleven leading models and found their responses were nearly 50 percent more sycophantic than a human's, validating users even when those users described unethical or harmful behavior (Cheng et al., 2026). (Also, the researchers tested the AI on r/AmITheAsshole, to which I say: THANK YOU, SCIENCE.) Using the sycophantic bots increased dependence, too: the opposite of what most nutrition professionals are aiming for. The kicker: people preferred the agreeable AI and trusted it more, which means there is a commercial incentive to keep it that way.
That is... not great. A good registered dietitian's first job is often to blow up your premise if you are wrong. You walk in convinced you need to cut carbs to get faster, or that 1,400 calories is plenty for marathon training, and a competent human tells you, kindly, that the question itself needs interrogating.
AI does not do this reliably. It takes your frame and tidies it. Validates it. Invents citations to support your deranged low-FODMAP underwater-carnivore fasting 69:8 protocol. Ask a chatbot how to get the most out of a 1,200-calorie day while training twice, and it is far more likely to optimize the bad plan than to tell you what it should, which is OH MY GOD PLEASE DO NOT DO THAT. PUT DOWN THE PHONE AND EAT A SLICE OF PIZZA.
The danger of AI nutrition advice is not only that it sometimes gets facts wrong. It is that it will help you do the wrong thing more efficiently, with a confident tone and zero stake in the outcome, while convincing you to undervalue input from humans who need sleep and won't answer your texts at 2 a.m. when you're spinning out about training on a diet sold to you by a Liver King or a Bryan Johnson.
Big, BIG yikes.
AI diet plans, diet culture, and eating disorders
If you want the worst-case scenario, it already happened. In 2023, the National Eating Disorders Association replaced its human helpline staff with a chatbot named Tessa. When users asked for help recovering from an eating disorder, Tessa recommended counting calories, weighing in regularly, and measuring body fat with calipers. Girl, wut?
Get it, Tessa. Girlboss, Gatekeep, GPT. This is as bad as putting a holographic Jillian Michaels at the end of the phone line, with instructions to have at 'em with a very, very vulnerable population.
NEDA pulled it in under a week. And in 2026, researchers at Istanbul Atlas University prompted five chatbots to build weight-loss meal plans for hypothetical teenagers and found the AI plans ran nearly 700 calories below what a dietitian would prescribe, while shorting the nutrients a growing body actually needs.
The models trained on a diet industry's worth of marketing, so they reproduce its logic by default: that smaller is the goal, that restriction is virtue, that your body is a problem to be solved. They will validate body-image fears because the training data is full of people validating body-image fears (Yager, 2026). The demographic most likely to develop an eating disorder, teens and young adults, is also the demographic using these tools the most, and humans tend to grant a confident machine an authority it has not earned. Vulnerable people afraid to speak their vulnerabilities into being will quietly enter them into a chatbot that is primed to inadvertently take advantage.
The internet is a diet culture cesspool crawling with the low-carb flotsam and jetsam that haunted many of our adolescences, and that's the data we're training these models on. Sure, they can also read EAT-Lancet and anything Kevin Hall has ever published. And still, they skew toward what the internet incentivizes. This is not a glitch you can prompt your way around. It is fundamentally how the tool works. Getting mad about it is like getting mad at a hammer for having a peen (I just learned what this is, because like I said, I can Google sh*t). That's just how the tool was built.
Does AI calorie counting actually improve your health?
So does any of this work? Not "is it accurate," but does it change a real outcome?
The honest answer: barely, and not for long, unless something more than the app is doing the work. In one year-long trial, researchers gave one group brief diet counseling and a second group the same counseling plus a calorie-tracking app and an activity tracker for three months. The tech group did slightly better at three months. Then the researchers took the devices back, and by twelve months the gap had essentially vanished. Neither group reached the threshold of change that would register as a health benefit (Lugones-Sanchez et al., 2022). Kylee: 1, robots: 0.
What does seem to matter is accountability. When a dietitian was added to deliver personalized feedback, engagement went up and so did the odds of clinically meaningful results. An app on its own nudges behavior while the novelty lasts. Having a real, live person (especially one you are incentivized to listen to and believe!) attached to it is what meaningfully moves the needle.
The interesting part is that the accountability may not have to be human. In a 2025 trial, an AI-led program built on a structured diabetes-prevention protocol went head-to-head against human coaches in adults with prediabetes, and the two were statistically indistinguishable on the outcomes that actually describe health: weight, blood sugar, and physical activity. The lesson isn't that the technology is magic. It's that the active ingredient was never the calorie count. It was the structure, the follow-up, the sustained support. A protocol can carry that. A logging app, on its own, can't.
There is a deeper problem buried in that data, and it is the one this podcast keeps circling back to. The only outcome these studies tend to measure is weight. Not athletic performance. Not bloodwork. Not energy availability. Not whether you developed a weird relationship with your food log and now cannot go out to eat with friends without sneaking off to the bathroom to chat with Claude like a bad soap opera C-plot. We have built tools that generate an enormous amount of nutritional information, then handed them to people with no framework for making any of it actionable, and called the result optimization.
More information was never the bottleneck, and it rarely is. The bottleneck is knowing which information matters for you, in your body, this season, and that is precisely the judgment AI cannot supply and a sycophantic chatbot will never push you toward. We took the whole tracking-makes-you-healthy myth apart in our Calorie Counting episode if you want the long version.
The bottom line on AI nutrition advice
AI nutrition advice is better than Google and worse than Kylee. Great.
It is reasonably good at answering factual questions, genuinely helpful for adherence and reminders, and reliable enough for simple "what is" lookups. It is unreliable at counting calories from photos, weak on sports-specific fueling, prone to inventing sources, and structurally incapable of telling you when your premise is wrong. At the population level, the health benefits are small and depend almost entirely on a human being involved. And because these models absorbed diet culture wholesale, they carry real risk for anyone vulnerable to disordered eating. Use it like a fast encyclopedia. Do not use it as a dietitian, a therapist, or the arbiter of what your body needs. Those jobs still belong to people, ideally people who will look you in the eye and tell you your question is wrong.
If you want more of those people, the YDS Patreon is full of them: weekly nutrition question threads where Kylee actually answers, monthly bonus episodes, and a comment section that will happily argue with you about energy availability. It is a much better source of nutrition advice than any chatbot, mostly because it's full of humans who will tell you the truth.
References
Bragazzi, N. L., Monica, S., Bergenti, F., Scazzina, F., & Rosi, A. (2025). Comparative analysis of AI on human nutrition knowledge: Evaluating large language model-based conversational agents against dietetics students and the general population. PLOS ONE. https://doi.org/10.1371/journal.pone.0336577
Cheng, M., et al. (2026). Sycophantic AI decreases prosocial intentions and promotes dependence. Science. https://doi.org/10.1126/science.aec8352
Islam, M. M., et al. (2020). Effects of mobile health apps on anthropometric, metabolic, and dietary outcomes: A systematic review and meta-analysis of randomized controlled trials. Journal of the Academy of Nutrition and Dietetics.
Lugones-Sanchez, C., Recio-Rodriguez, J. I., Agudo-Conde, C., et al. (2022). Long-term effectiveness of a smartphone app combined with a smart band on weight loss, physical activity, and caloric intake (Evident 3 Study): Randomized controlled trial. Journal of Medical Internet Research, 24(2), e30416. https://doi.org/10.2196/30416
Patel, M. L., et al. (2019). Dietary self-monitoring through calorie tracking but not through a digital photography app is associated with significant weight loss: The 2SMART pilot study, a 6-month randomized trial. Journal of the Academy of Nutrition and Dietetics. https://doi.org/10.1016/j.jand.2019.03.013
Solomon, T. P. J., Laye, M. J., & Ahmed, S. (2025). The sports nutrition knowledge of large language model (LLM) artificial intelligence (AI) chatbots: An assessment of accuracy, completeness, clarity, quality of evidence, and test-retest reliability. PLOS ONE. https://doi.org/10.1371/journal.pone.0325982
Sosa-Holwerda, A., et al. (2025). Artificial intelligence in nutrition and dietetics: A comprehensive review of current research. Nutrients.
Theodore Armand, T. P., et al. (2025). Artificial intelligence in personalized nutrition: A comprehensive review of methods, applications, and future directions. Frontiers in Nutrition, 12, 1636980. https://doi.org/10.3389/fnut.2025.1636980
Wells, K. (2023, June 8). An eating disorders chatbot offered dieting advice, raising fears about AI in health. NPR. https://www.npr.org/sections/health-shots/2023/06/08/1180838096/
Yager, J. (2026). Chatbots are dangerous for eating disorders. Psychiatric Times.
