First principles thinking is often described as a foolproof method for tackling complex challenges in engineering, entrepreneurship, and scientific research.

A structured but flexible way to practice first principles thinking can be understood as a cycle with seven overlapping phases. The seven phases are Attunement, Observation, Intuition, Transformation, Analysis, Execution, and Iteration.

1. Attunement

Pay attention to what’s going on.

• What it is: The habit of noticing subtle shifts and signals in your surroundings. It’s about giving yourself time and mental space to perceive details—without judging or labeling them too quickly.

• Why it matters: When you’re attuned, you spot early indicators that something might need a second look. It could be a slight temperature increase in a prototype device, or the uneasy silence of a team member who might have valuable feedback but hesitates to speak up. These early observations are your “clues” that guide deeper inquiry.

• Link to first principles: If you never notice the small cracks in an existing system, you won’t investigate the underlying issues that could lead to a radically better design or approach. Attunement opens the door to the entire process.

• Bakery example: In a small bakery, staff members casually note how dough behaves differently on humid days, how the oven’s sound changes when overloaded, and which bread types linger on shelves. They aren’t labeling anything as “good” or “bad” yet; they’re simply open to hints that may later prove significant.

• Hunting example: A seasoned hunter steps into the forest and immediately notices the direction of the wind, faint animal tracks by a creek, and subtle rustlings in the brush. Rather than rushing to conclude what’s out there, the hunter stays open to these signals, recognizing that every detail could lead to a successful hunt.

• SpaceX Engineer example: An engineer at SpaceX pays attention to subtle variations in rocket engine performance during test firings—perhaps a slightly different pitch in the engine’s roar or a minor fluctuation in temperature readings. They don’t jump to conclusions about a fault yet, but remain primed to investigate if these small changes signal a deeper issue.

2. Observation

Track the facts and figures.

• What it is: Collecting measurable facts and figures rather than relying on impressions. You move from “I think we’re throwing away a lot of extra bread” to “We bake 200 loaves daily, sell 150, and toss 50.”

• Why it matters: Data is the bedrock of rigorous thinking. Without it, any perceived “problem” might just be a hunch. Actual numbers, times, or other quantifiable data validate your assumptions—or reveal where your impressions are off.

• Link to first principles: Gathering data ensures you’re working with reality. Engineers use tools like sensors, prototypes, and test rigs to measure performance; entrepreneurs track metrics like user retention or revenue. Accurate observation feeds the rest of the cycle.

• Bakery example: Once the bakery team decides to measure leftover bread more precisely, they track each day’s bake count, the exact time loaves are sold, and which types of bread consistently remain unsold. By converting impressions into concrete data, they lay a foundation for identifying inefficiencies and patterns.

• Hunting example: The hunter notes the size of hoof prints, the depth of paw marks, and the times of day when animals seem most active. They might also record wind direction, temperature, and even the moon phase, creating a log that goes beyond vague memories and forms a reliable reference for future hunts.

• SpaceX Engineer example: Engineers compile detailed sensor data from each rocket test: thrust measurements, fuel consumption rates, heat distribution, and vibration patterns. They also log anomalies in telemetry data to build a clear, quantifiable picture of the rocket’s performance over multiple test runs.

3. Intuition

Sense that something needs fixing.

• What it is: A gut-level sense that something isn’t right or could be better, grounded in the observations you’ve collected. It’s often a feeling that grows after you’ve accumulated enough data to see a pattern, but not enough to fully articulate it yet.

• Why it matters: Intuition often tells you there’s more to be discovered—even before you can articulate the details. It’s a signal to dig deeper. For example, you might sense that leftover bread in a bakery isn’t just about waste; it might point to an ill-fitting production schedule or misplaced priorities.

• Link to first principles: Your intuition often highlights the cracks in your initial assumptions, nudging you to search for deeper truths. It’s an emotional or subconscious bridge between raw data and targeted questioning.

• Bakery example: As the bakery’s data piles up, staff members notice a recurring pattern of half-full racks at closing time. A quiet discomfort builds: they sense that throwing away perfectly good bread indicates a deeper inefficiency. Although they haven’t formally labeled the problem yet, this feeling pushes them to question their assumptions.

• Hunting example: After studying footprints and noticing changes in local wildlife behavior, the hunter might get a hunch that a predator has entered the area—perhaps explaining why game animals seem more scattered than usual. Even if they don’t have hard proof yet, something about the patterns feels off, prompting them to investigate further.

• SpaceX Engineer example: Despite a stack of performance metrics, an engineer might sense there’s a deeper issue when minor inconsistencies in fuel flow persist across multiple tests. A gut-level feeling tells them this pattern isn’t random, hinting at a design flaw or overlooked factor that needs deeper exploration.

4. Transformation

Turn your hunch into a question.

• What it is: Turning that vaguely unsettling feeling into a precise problem statement or question. “We’re wasting bread” becomes “How can we reduce bread waste without compromising freshness?”

• Why it matters: A clearly formulated question guides your next steps. If you stay at the level of “I think we’re doing something wrong,” you’ll run in circles. A question like “How do we fix X?” or “What if Y was done differently?” transforms the issue into something testable.

• Link to first principles: Articulating the question is like setting your sights on the core objective. Engineers might say, “How do we optimize our cooling system for minimal energy use?” Entrepreneurs might ask, “How do we serve more customers with fewer wasted resources?” Transformation ensures you’re solving the right problem.

• Bakery example: Seeing bread wasted every night, the bakery forms a direct question: “How can we reduce leftovers without compromising freshness?” This once-blurry concern about inefficiency is now sharpened into a well-defined goal that focuses the entire team’s efforts.

• Hunting example: The hunter transforms a gut feeling about reduced game activity into a clear question: “Is there a new predator nearby, and if so, how can I adjust my tracking strategy?” This shifts the exploration from an uneasy hunch to a defined problem to solve.

• SpaceX Engineer example: Convinced there’s a deeper cause to the fuel flow inconsistencies, an engineer might ask: “What underlying design or hardware variables could be causing pressure imbalances during engine burns?” By framing this question precisely, they zero in on the real challenge.

5. Analysis

Challenge every assumption you have.

• What it is: A systematic teardown of the status quo. You look at every practice, assumption, or “rule” that your operation relies on and ask, “Why are we doing this? Is it actually necessary or beneficial?”

• Why it matters: Many inefficiencies hide in routines that nobody questions. Analysis digs them up. If a bakery bakes everything at 5 a.m. regardless of demand, or if a startup invests in a marketing channel that yields no users, it’s essential to identify these faulty assumptions so they can be eliminated.

• Link to first principles: This is where you challenge conventional wisdom. Engineers often recheck basic laws or constraints to see if certain design features are mandated or if they’re just assumed. Entrepreneurs question whether they really need a physical storefront or a large sales team. Analysis helps you find the core truths rather than inherited beliefs.

• Bakery example: With the direct question in mind, the bakery team scrutinizes their routine: Must they bake everything in large batches first thing in the morning? Do customers really need ten different bread varieties every day? Through these inquiries, they learn which aspects of their process truly matter—discovering, for example, that customers do care more about freshness than about having a huge array of options at all times.

• Hunting example: The hunter reevaluates each part of their usual strategy, asking whether pre-dawn setups or typical trail routes still make sense. They review assumptions about animal migration patterns, water sources, and weather conditions, discarding those that no longer match the data they’ve gathered.

• SpaceX Engineer example: Engineers challenge each part of the rocket’s design and testing protocol. They might ask if specific engine components are necessary or if they’re relying on outdated calculations for fuel mixture ratios. By comparing data against their foundational physics and engineering principles, they clarify which elements are truly critical.

6. Execution

Put your new ideas into action.

• What it is: Putting your conclusions and newly revealed truths into action. You adopt new strategies, test prototypes, reorganize schedules, or launch pilot programs to see if your ideas hold water.

• Why it matters: No plan survives contact with reality until you test it. Real-world conditions might reveal unforeseen variables or confirm that you’re on the right track.

• Link to first principles: Execution is about “walking the talk.” An engineer might implement a new circuit design based on first-principles insights. An entrepreneur might pivot the entire business model. If your fundamental reasoning is sound, your execution will offer compelling results—or yield new data to refine further.

• Bakery example: They begin baking smaller batches multiple times a day, reduce some low-demand specialty loaves, and offer a pre order system for regulars. These changes directly address the leftover-bread issue, enabling the team to see immediate effects on waste reduction and customer satisfaction.

• Hunting example: The hunter adjusts tactics based on their analysis—maybe switching to a different zone at a different time of day or using a new scent masking method. They observe whether these changes improve their odds of a successful hunt.

• SpaceX Engineer example: Guided by the refined question and deeper analysis, the team tests new engine nozzles, adjusts fuel pump configurations, or modifies sensor placements. Real-world trial runs help confirm if these refinements solve the pressure imbalance or uncover new challenges.

7. Iteration

Refine your approach again and again.

• What it is: Evaluating the effectiveness of your changes, learning from the outcomes, and adapting accordingly in a continuous loop.

• Why it matters: Even the best plan can’t foresee everything. Iteration keeps your solution evolving alongside new evidence or shifting circumstances.

• Link to first principles: By iterating, you maintain a cycle of questioning and refining. Engineers run multiple test cycles to optimize performance; entrepreneurs run A/B tests and adapt based on feedback. Iteration is what separates static ideas from truly dynamic, future-proof solutions

• Bakery example: After a few weeks of these smaller, spaced-out bakes, the team checks whether leftover bread has dropped. If certain loaves remain unsold, they adjust their baking schedule again or tweak which varieties they offer. Each iteration brings them closer to an ideal process that meets both operational and customer needs.

• Hunting example: The hunter evaluates how well the new strategies worked—did they see more game activity, did they confirm the presence of a predator, or was it a false lead? Based on the results, they refine their methods further, continuously honing their ability to adapt.

• SpaceX Engineer example: The engineering team reviews fresh test data to see if the changes truly resolved the fuel flow inconsistencies. If partial success or new anomalies arise, they refine the design again, rerun tests, and remain flexible to altering course as new insights emerge.

By moving through the seven phases—Attunement, Observation, Intuition, Transformation, Analysis, Execution, and Iteration—you learn to recognize subtle signals, gather real data, and pinpoint core challenges. Each phase builds on the last, transforming vague hunches into concrete questions, systematically testing assumptions, and refining solutions in a continuous loop. This structured yet flexible approach ensures that each step is guided by real insights, paving the way for sustained innovation and practical results.