AI for energy

You already know which days feel awful. AI helps you find out why — and what you can change.

Start the free 10-day challenge →See the method

What we’re actually working with

Energy is the subjective experience of recovery, glucose, sleep, hormones, and stress all at once. It's the most useful signal you can capture in 5 seconds a day.

Why doing this without a method fails

Most people describe their energy as 'random'. It almost never is. The pattern is there in the data you already collect.

The 3-Layer treatment

How the method handles energy

Layer 01

Research

Read what the literature actually says about the strongest modifiable inputs to subjective energy (sleep, training, light exposure, meals, social stress).

Layer 02

Ledger

Combine 90 days of a 1-line daily energy score with sleep, HRV, training, meals, and screen time. Let AI rank the inputs by predictive power.

Layer 03

Protocol

Test the top-ranked input over 4 weeks with a clean comparison. Decide whether to keep, drop, or refine it.

Read the full 3-Layer method →

Three prompts you can use today

Paste any of these into the AI chat tool you already use. No setup.

What predicts a good day?

Here's 90 days of daily energy (1–10) plus sleep, HRV, RHR, training, and a 1-line meal note. Rank the inputs by how well they predicted my best 10 days vs. my worst 10 days.

Crash pattern

I crash around 3pm most days. Here's my morning routine, breakfast, lunch, training, and energy score for 30 days. Find the inputs that protect against the crash and the ones that worsen it.

Light and energy

Design a 4-week test of morning light exposure (10–20 min within an hour of waking). Use my current energy score as the baseline and define what a meaningful improvement looks like.

Tools that help

How AI tools make energy easier to live with — and understand.

You don’t need another app. These are the tools most people already have or can use for free, and the specific job each one does when you point it at energy.

Research the literature

A sourced-search AI (e.g. Perplexity, ChatGPT search, Gemini)

Replaces an afternoon of tab-juggling on energy with a cited summary in minutes. Ask it to mark every claim as primary study, review, or opinion — that one habit removes most of the noise.

Read your own data

A long-memory chat AI (e.g. Claude, ChatGPT, Gemini)

Paste weeks of notes, exports, or symptom logs about energy in a single window. The AI spots patterns your seven separate apps hide from you, and remembers them next week.

Capture without friction

Apple Health + Notes (or Google Fit + Keep)

Already on your phone. Pulls energy-relevant signals into one export and lets you jot context in seconds — no new subscription, no new dashboard to maintain.

Stream the raw signal

Your wearable (Oura, Whoop, Garmin, Apple Watch)

Stop reading the marketing score. Export the raw stream behind your energy number and feed it to a chat AI — that's where the actual insight lives.

Build your own reference

NotebookLM (or any source-grounded notebook)

Drop in your lab PDFs, saved articles, and personal notes on energy. Ask questions; the answers cite back into your own sources. Becomes a second brain you actually trust.

Turn data into a plan

A weekly review prompt

One scheduled prompt every Sunday: "Given this week's energy data and notes, what changed, what's noise, what's the smallest experiment for next week?" Replaces three productivity apps and an anxiety spiral.

Browse the AI Tools Atlas →See how the method uses them

Common questions

Isn't 'energy' too subjective to analyse?+

On its own, yes. Combined with objective data, a daily 1–10 score becomes one of the most useful signals you can collect.

Do I need a wearable?+

Helpful, not required. A simple notebook with sleep duration, training, and an energy score works.

What if nothing predicts my energy?+

Then the answer is usually missing variables (light, hydration, social stress, hormones). AI can help you find them.

Deeper research

The evidence — and where it breaks down

Six short briefs on what the literature, the devices, and the AI tools actually do when you point them at energy. Read them before you change anything.

What the current research actually says about energy+

Energy is the subjective experience of recovery, glucose, sleep, hormones, and stress all at once. It's the most useful signal you can capture in 5 seconds a day. Most peer-reviewed work on energy sits in three buckets: mechanistic studies (small samples, tightly controlled), observational cohorts (large samples, noisy variables), and consumer-device validation papers (mixed quality, often vendor-funded). When you read AI-generated summaries on AI for energy, treat the first two as signal and the third as buyer-beware. The 3-Layer method makes you triage these before they enter your personal ledger.

What your wearable or app is really measuring (and what it isn't)+

Consumer devices that surface a "Energy" score almost always combine a small set of raw signals — accelerometry, optical heart rate, skin temperature, sometimes ECG — into a proprietary index. The score is opinionated, the raw stream is not. The Ledger layer of the method exports the raw stream so AI can analyze the underlying variables instead of the marketing score. That is where most insight lives.

Where consumer-grade energy data is reliable vs noisy+

Cross-validation studies (Stanford, ETH Zürich, and several EU centres in 2023–2025) consistently show that wearables are most reliable for trend direction and least reliable for absolute values — especially night-to-night energy. Use the data the way it is actually accurate: deltas over weeks, not single-night verdicts. AI is well-suited to this kind of rolling-window analysis; humans staring at one number are not.

Common confounders that distort energy signals+

Most people describe their energy as 'random'. It almost never is. The pattern is there in the data you already collect. The most under-discussed confounders are time-of-month variation, recent travel, alcohol with a 48–72 hour tail, ambient temperature, and any acute infection — all of which shift baseline values by more than most behaviour changes do. A good AI ledger tags these as covariates before drawing conclusions; a bad one quietly attributes the swing to whatever supplement you started that week.

What "good evidence" looks like — and what's hype+

Good evidence on energy: pre-registered protocols, declared funding, raw data available, effect sizes reported with confidence intervals, replication in an independent cohort. Hype: single n-of-1 anecdotes generalised on social media, supplement-funded reviews, AI summaries that cite nothing. Read what the literature actually says about the strongest modifiable inputs to subjective energy (sleep, training, light exposure, meals, social stress). Asking AI to mark every claim with "primary study", "review", or "opinion" before you act on it is one of the most useful prompts you can run.

How AI changes the picture for energy in 2026+

Three shifts matter. First, long-context models can now read 60–90 days of your raw export in a single pass and find correlations no app dashboard surfaces. Second, sourced-search models (with citations) collapse the literature-review step from days to minutes — provided you verify the citations. Third, agentic workflows can run the same daily check-in you would otherwise skip. Test the top-ranked input over 4 weeks with a clean comparison. Decide whether to keep, drop, or refine it. The judgement layer — what to test, what to ignore, when to stop — is the part that stays with you.

Educational summaries — not medical advice. Cross-check claims against primary sources before changing anything material.