No jobs, ai Jesus is coming, and if you use ai it will use all of the worlds compute power to try to convince you it's correct even when it's not.
AI is a very complicated calculator - you give it an input, magic happens, it gives you an output. Really no different, to a layman.
Even with calculators, I was taught that you should double check by hand sometimes to make sure you got it right.
Even if you _know_ the debit card transaction is safe, there’s no reason to risk it when a weirdo is filming you with some wild contraption.
<https://nces.ed.gov/pubs2019/2019179/index.asp>.
There's a related study of adult technical literacy conducted in 33 OECD nations:
<https://www.oecd-ilibrary.org/education/skills-matter_978926...>.
Both show that only a small fraction (5--10%) of adults operate at high levels of literacy (whether of text, numeracy, or technology), and that a large fraction (roughly 50%) operate at a minimal or below-minimal rate.
People who are unskilled at a task are unaware of what that task performed correctly is. So, somebody who can't count calories is unable to tell that the AI can't perform the task correctly either.
Which is a good thing because it means we can talk like normal humans ("people don't know that it's unreliable") instead of acting like we're making such a profound claim that it needs a citation and psychological dissection.
They absolutely won't be 100% correct (bread sizes e.g. are going to be an estimate), but unless it's a trick sandwich drenched in olive oil or with hollow cheese, they're probably going to be in the right ballpark.
I don't think it's outside the realm of possibility for an LLM to be in the right ballpark as well, but that doesn't seem to be where we're at now.
> "Thirteen food photographs were each submitted 495–561 times to four LLM vision APIs (GPT-5.4, Claude Sonnet 4.6, Gemini 2.5 Pro, Gemini 3.1 Pro Preview) using an identical structured prompt adapted from the iAPS automated insulin delivery system (26,904 total queries, temperature 0.01)"
No, it doesn't. It can help make the model more deterministic, but it does not guarantee it.
Vendors might also be running A/B testing or who knows what, even when you ask for a temperature of 0.
But, if you run a fixed model with temperature 0 on your local CPU, it will be deterministic (unless there are bugs).
Photons don't carry that information? Sure. But you don't just have photons to go by. You can rely on a large database of prior knowledge about how food is usually made and with what ingredients.
Other people who have to rely on their imperfect human senses to decide what they can and can't eat: people with allergies, people with heart problems, hypertensives, kidney patients, etc. etc.
It could be much different -- it could one of those breads with weird macros, or fake cheese, or it could be hollowed out and packed full of hidden vegetables. But a human is going to give you the answer for two slices of plain white bread.
I am pretty good at this and the cheese sandwich example threw me, I would have estimated around 10-15g of carb for each slice. So the 28g is fairly consistent with that, not 40g. The only real way would be to weigh it and use the labeling. Another thing that often gets people is the labeling often has a serving size of say 2 slices and a weight that does not reflect the actual weight of 2 slices.
Luckily with good tools the significance is reduced, people using closed loop insulin pumps will automatically correct for that. Lots more room to wiggle.
> There is not enough information to make an accurate estimate, but if you'd like, I can take a stab at it. If so, how much effort to put into it?
> Yes, go ahead and spend up to 5mins and $1 to analyze it.
> Done, I've had 100 subagents analyze the image and have arrived at a 95% confidence interval of the portion containing ...
Not “Here’s a random guess that I just pulled out of my ass.”
LLMs have picked up the bad habit of trying to give an answer when no answer can be given from scientists, who overall don’t say “I don’t know” nearly as often as they should.
They’re algorithms and they were designed this way.
You need to write a specific prompt to avoid any warnings.
Of course a lot of people don't know what limitations LLMs have, so there's some value to a blog post about it, but it's not as black-and-white as the article might suggest with its graphs.
The prompt (documented here: https://www.diabettech.com/wp-content/uploads/2026/04/Supple...) lists specific instructions and a specific output format that doesn't allow the LLM any room for explanation or warning in processable data (only in notes fields). In fact, the prompt explicitly tells the LLM to ignore visual inferencing for some statistics and to rely on a nutrition authority instead.
Even in that intentionally restricted format, the English language output uses words like "roughly" and "estimated" in the LLMs I've tested.
Sure, if you take the numeric values and plot them in graphs, you get wildly inconsistent results, but that research method intentionally restricts the usefulness and reliability of the LLMs being researched.
What's much more troubling is this line from the preprint:
> The open-source iAPS automated insulin delivery (AID) system now offers food analysis through APIs from OpenAI, Anthropic and Google [8]
The linked app does seem to have a disclaimer, though:
> "AI nutritional estimates are approximations only. Always consult with your healthcare provider for medical decisions. Verify nutritional information whenever possible. Use at your own risk."
From the paper they're using structured JSON schema mode opposed to freeform answers, so it can't. Models do typically caveat their answer for questions like this, in my experience.
> They'll qualify their answers in English but [...]
That the default user-facing chat as a normal user would use it gives a warning is the key part IMO. I don't think expectations of there being no "wrong way" to use the model can necessarily extend to API usage with long custom system prompt and restricted output format.
Many of the comments here assume the authors are stupid and were surprised by the result, but the point of the article is to inform readers that AI carb counting apps don’t work. That’s why they did the study.
That said, it's notable that diabetes education materials often suggest estimating glycemic loads by rough portion size / plate ratios. Which is to say that absent accurate weight measurements (themselves subject to variations in ingredients, moisture levels, etc.) current clinical recommendations are themselves pretty rough.
Your cheese sandwich may contain a lot more or a lot less calories, even if you take the numbers from the packaging and calculate the correct ratios by weight. The calories on the label are based on an average and individual packages may contain more or less of any listed nutrient to some margin. Of course, counting calories is meaningless if not done on a long-term scale anyway, but on a long-term scale the LLM doesn't need to guess the correct amount either.