Is AI Accurate at Counting Calories? (Or Does It Actually Matter?)

AI is obviously all the rage right now, and even nutrition apps are jumping on the bandwagon with wanting to be able to say, “AI-powered this” and “AI-powered that.”

People are understandably skeptical about AI for a lot of reasons, obviously. But with nutrition apps specifically, one question that gets asked a lot now is, how accurate is AI at counting calories?

This question was a big part of building the CalorieLite app. Not just how accurate is AI, but also how could an app help AI be more accurate? Or how could an app help manage AI’s limitations? And ultimately, can we make an AI-assisted calorie tracking app that not only looks slick, but is actually worth using over a traditional app?

I’ve tested a lot of entries at this point, comparing what AI calculates versus what the nutrition facts or manual measurements actually say. I’ve also done this in conjunction with developing the prompt engineering in the app’s back end as well, because part of making AI accurate is giving it the best possible instructions to help it be so.

I want to clarify that I’m only talking about text-based prompts here, not picture prompts where users take a photo of their meal and then AI uses the pictures to approximate calories. That’s a whole different discussion.

Here’s what I’ve found with text-based prompting: If AI is utilized in the right way, it can be very accurate at counting calories, plenty enough so for the average person. And in fact, AI can actually do a better job than humans can in certain situations. AI itself and prompt engineering are only going to keep getting better too.

That said, AI still does have its limitations and the less you understand them or understand how to manage them—and this is true both as a user and a developer—the more the calorie count calculations can be off. And sometimes they can be off by a lot.

Getting into the full specifics of this would make a very, very long article. Instead I want to focus on a more practical issue with the “Is AI accurate?” question. And it’s that it kind of misses the point in the same way that trainers and coaches often also miss the point when they talk about calorie tracking.

The Issue with the “Is AI Accurate at Counting Calories?” Question

The bottom line is that, AI or no AI, calorie counting can never be truly accurate. And figuring out the exact truth of your calories-in, calories-out energy equation is practically impossible, no matter what.

There are a lot of reasons for this, but I’ll give a few examples:

One, nutrition facts labels themselves aren’t always exact. The FDA gives food manufacturers allowable margins of error for what they report on their labels.

Two, unless we’re using a food scale at home and measuring each ingredient to the gram, our own measurements via eyeballing a portion on our plate or in a measuring cup are never going to be totally consistent.

Three, there’s the whole expenditure side of the equation. How many calories do we actually burn in a given day? Outside of a closed lab that’s actually measuring the precise amount of oxygen we consume and carbon dioxide we produce, all we can really do is estimate.

So we tell an app we took 10,000 steps today, but were they long, brisk steps or short, leisurely steps? How much did we truly exert ourselves at the gym today? Or while doing chores around the house? It’s just not practically possible to know exactly how many calories we burn on a regular basis.

So point being here is that, regardless of AI, calorie tracking is at best a game of educated guesses.

What Do We Do If Calorie Tracking Can’t Be Accurate to Begin With?

There’s an obvious follow-up question now: how can we lose weight effectively if tracking calories accurately isn’t actually possible?

Well, the first thing to realize is that our tracking doesn’t have to be totally accurate. As long as we’re within ~10% of our target, that’s going to be close enough to make a meaningful difference over the long run. Maybe we ate 5% over the calorie target today, but maybe we ate 7% under it yesterday. And if we’re close enough most days, that’s going to be good enough for the average person to facilitate meaningful progress.

But there’s something more important than that: even though we can’t make our tracking perfectly accurate, what we can do is be consistent in how we track. Doing that gives us something meaningful and useful: a well-defined baseline of behavior that we can adjust off of.

Consistency Actually Matters Much More than Accuracy Does

This is the aforementioned point that people often miss when it comes to calorie tracking. Calorie tracking isn’t effective because it’s accurate; it’s effective because it helps establish the consistent behavior that is required to yield meaningful changes.

I think the best way to explain what I’m trying to convey here is with a hypothetical example.

Let’s suppose someone uses a calorie calculator which determines that they need to consume 2,200 calories per day to lose weight. Let’s also suppose that this person makes the same basic breakfast and lunch every day, and logs each as 600 and 800 calories, respectively.

Now let’s say this person sticks to their program for about a month, but the scale doesn’t change. So there’s an issue somewhere.

Maybe the calorie calculator got their target wrong, or maybe they thought they were burning 2,700 calories a day, but in reality it’s more like 2,300. Or maybe they undercalculated the calories of said breakfast and lunch, or maybe the way they’re describing what they ate to an AI tracker is causing it to underestimate calories. Or it could be a combination of all these things.

Now this person could try to figure out where the error or errors truly are, but good luck actually doing that outside a science lab, for one. And two, they don’t actually need to do that. If they’re being consistent with how they’re doing things and tracking, they can make a meaningful enough adjustment somewhere in the equation. And it doesn’t really matter where that adjustment is.

So let’s say they decide that they underestimated the calories of their breakfast and lunch, and so they reduce the portions accordingly. Then they start losing weight, and the problem’s solved. It could be that in reality their breakfast and lunch calorie calculations were actually correct, and that it was actually the calorie calculator that they used that was wrong because it overestimated their daily energy expenditure.

But like I said, it doesn’t really matter. What does matter is that this person made a meaningful adjustment somewhere in the math of their calorie balance equation to get it where it needs to be. And the reason that they were able to even do that at all is that they were being consistent in how they were defining and doing things to begin with.

Now let’s compare that to some other person who says something like, “I want to lose weight, so I’m going to eat cleaner, do some intermittent fasting, remove inorganic stuff, and work out more.” Let’s suppose they similarly do their system for a month and don’t see the progress they were hoping for. The problem now though is, how does this person make a meaningful adjustment to try to fix the problem?

They can’t, because with vague behavioral guidelines, it’s not really clear what they were actually doing to begin with. You can’t make a meaningful adjustment from point A to point B when you don’t know what or where point A actually is.

Why AI Is Actually Such a Powerful Nutrition App Enhancer

So coming back full circle to the original question, is AI accurate at counting calories? No, it’s not perfect. And sometimes you do need to give it a quick look-over and make sure it passes a common-sense sniff test. But for the most part, it’s pretty close. And about as close to what we would get if we did it ourselves manually.

But AI being “accurate” isn’t what actually makes it a powerful enhancement to nutrition apps. As I’ve developed and used CalorieLite myself, what I’ve realized is the reason AI is so useful is that it helps automate things.

AI can take a spoken description like, “I had an eight-ounce filet steak, a medium-sized baked potato with a little butter and cheese, and some asparagus sautéed in olive oil,” and it can automatically format it into a data set that a tracking app can understand and log to your diary for you.

Compare that to doing it yourself the old way: figuring out the individual foods or ingredients, finding them in a long database list, specifying the amounts, entering them one by one. Scrolling and thumbing through lists and windows and buttons every. single. time. you need to log a food.

All that decision fatigue and action fatigue really adds up. And I think for a lot of people, it’s the difference between being willing to stick with calorie tracking or not.

The power and usefulness of AI for nutrition apps comes from it letting you skip all that work. And this is the UX principle I really leaned into when I designed CalorieLite, which is that if I make the logging process as easy as possible—one button—the more likely I am to consistently do it, and the less it feels like a dreadful chore.

Because at the end of the day, the only thing that really matters is that you either do something or you don’t. And if you can stay above the motivational threshold to keep doing something and that something is good enough to get the results you want, then that’s all that really matters.