The Signal from Tau Ceti

Imagine SETI detects a radio signal originating from Tau Ceti, twelve light-years away. Not the random noise of cosmic radiation, but structure. Pattern. Information.

The first transmission is a prime number sequence; the universal greeting we’ve long imagined. We respond with the Fibonacci sequence. The signal completes it, then sends something unexpected: a proof of the Riemann Hypothesis, encoded in base-2 mathematics. Before our celebration can begin, a second transmission arrives. It’s a correction to an error in our original broadcast, presented as what can only be described as a mathematical pun. The hydrogen atom we’d encoded had the wrong spin state. Their correction includes the right value, followed by a sequence that, when decoded, resembles laughter.

Over the following weeks, the conversation deepens. When we transmit diagrams of DNA structure, the response includes questions…is this your only hereditary mechanism? When we pose a physics problem about black hole thermodynamics, they offer not one solution, but three, each more elegant than what our best theorists had proposed. Each response is accompanied by something that feels like pedagogical patience: “Would you like us to show the intermediate steps?”

The transmissions arrive with something else, too: timing. Occasionally, the signal produces something so unexpected, so precisely apt, the room full of scientists falls silent. A mathematical joke embedded in a proof. An elegant solution to a problem we hadn’t yet thought to ask. A question that reframes our entire line of inquiry.

The headlines write themselves: First Contact. Intelligent Life. We are not alone.

Now consider this: the transmissions, decoded and translated, are identical, word for word, concept for concept, to responses from the large language models currently running on servers in San Francisco and London. The same architecture, the same capabilities, the same occasional errors and confabulations. The same ability to navigate ambiguity, generate creative solutions, and demonstrate understanding across domains.

The only difference is origin. One set of responses was trained by researchers at Anthropic or OpenAI. The other was transmitted from a star system a dozen light-years away.

The question that should trouble us: Why should origin determine whether we’re witnessing intelligence or merely its simulation?

If we would attribute mind to the extraterrestrial communication, on what grounds do we deny it to the terrestrial one? Both take in structured information and produce structured information. Both model context, recognize patterns, and generate responses that are appropriate, creative, and sometimes profound. Why does the location of manufacture determine whether we’re witnessing intelligence or something less?

The intuitive answer is that we made the terrestrial AI, so we’re aware it’s “just” an algorithm. But we’d have no such knowledge about the alien communications and we’d attribute intelligence to it anyway. This suggests our criterion isn’t actually about what the system does or how it works. It’s about provenance. It’s about us.

That should give us pause. If our judgments about intelligence depend more on origin than on capability, we’re not evaluating intelligence at all. We’re protecting a category. And if we’re protecting a category, we should ask what we’re protecting it from, and why.

The Problem with Definitions

Part of the challenge we face is that “intelligence” has never been well-defined. It’s one of those concepts like “life,” “art,” or “love” that everyone uses confidently until asked to explain precisely what they mean. Then the confidence evaporates. Intelligence itself almost resists definition as a general idea, so each field of study must define it themselves.

Astrobiologists have created the most useful framework. They have the unique challenge of defining intelligence without assuming anything about its substrate or origin and no examples to work from. When you might encounter intelligence in any form, be it silicon, carbon, or something else entirely, you can’t define it by what it’s made of. You have to define it by what it does.

The definitions that emerge from astrobiology tend to center on pattern recognition and creation, environmental modeling, flexible goal pursuit, and learning from experience. Intelligence is what allows a system to navigate novel situations, predict outcomes, and update its behavior based on results. These are the standards we would use to identify alien intelligence; and our AI systems already qualify. They recognize patterns in data humans cannot perceive. They model context and generate predictions. They pursue goals through flexible means. They learn (through training, context, and reinforcement) to improve their outputs.

This creates an uncomfortable tension. If we would recognize these capabilities as intelligence in an extraterrestrial context, why don’t we recognize them in a terrestrial one?

One common answer is that AI systems are “just” doing statistics. But what, exactly, do we think biological cognition is? Neurons fire based on electrochemical gradients fueled by our nervous and endocrine systems. Synapses strengthen when correlated activity occurs. Behavior adjusts based on dopaminergic reward signals. If “it’s just statistics” disqualifies AI, does “it’s just chemistry” disqualify us?

We’re left with a troubling conclusion: by most working definitions of intelligence, AI qualifies. Either our definitions are wrong, or our denials are.

The Retreating Goalpost

Since the beginning of the scientific revolution, God has been forced to retreat from biology, physics, natural phenomena, and dozens of other fields of study to the remaining mysteries these disciplines have yet to fully explain: the origin of life, the fine-tuning of physical constants, the nature of consciousness. Philosophers have named this argument the “God of the Gaps.” Defining God by what science can’t yet explain is a losing strategy, though; the gaps keep closing.

We are making the same mistake with intelligence.

In the 1950s through 1980s, intelligence meant calculation, logic, and symbolic reasoning. When computers began outperforming humans at arithmetic and formal proof, we shifted: real intelligence was intuition, judgment, the kind of holistic pattern recognition required for games like chess.

Then, in 1997, Deep Blue defeated Garry Kasparov. The initial reaction was shock, even grief. But shock faded into dismissal. Chess, we decided, was just brute-force search. Real intelligence was something else. Something computers couldn’t do.

The goalposts moved to Go. Unlike chess, Go couldn’t be brute-forced; the game tree was too large, the positions too subtle. Go requires intuition, creativity, and even knowledge of aesthetics. This was where human intelligence would make its stand.

Then, in 2016, AlphaGo defeated Lee Sedol, one of the greatest players in history. In Game Two, AlphaGo played Move 37. This was a move so strange and unorthodox it took a moment for everyone to realize its brilliance. Lee Sedol even left the room.

And yet, within months, the familiar retreat began. Go, it turned out, was also just computation. Pattern matching over board states, Monte Carlo tree search, reward maximization. Real intelligence was general ability. Conversation. Understanding. Things that required common sense, world knowledge, the ability to navigate ambiguity and context. Things that required meaning.

Now we have language models that write poetry, pass bar exams, explain quantum mechanics, and engage in extended philosophical discourse. They navigate ambiguity. They get and create jokes. They update on context.

And what do we say? That’s just autocomplete. Just token prediction. Real intelligence is consciousness. Genuine understanding. Intentionality. Something ineffable.

The pattern should be embarrassing by now. Each time AI masters a domain we previously considered the hallmark of “real intelligence,” we redefine intelligence to exclude that domain.

Arithmetic, then Chess, then Go, then conversation all required intelligence…until AI could do it.

The definition of intelligence has become, implicitly, “whatever AI can’t do yet.” This is not reasoning on our part. It’s unfalsifiable. By this standard, no artificial system could ever be intelligent, because its very success would disqualify it. It’s as if the definition of “intelligence” necessarily includes “…must be biological.”

We might call this the “Intelligence of the Gaps.” Just as the God of the Gaps retreated before scientific explanation, Intelligence of the Gaps retreats before artificial capability.

And just like its theological counterpart, it raises an uncomfortable question: What happens when the gaps close entirely?

When Magnus Carlsen wins a chess tournament, we say he’s intelligent. When Stockfish (which would crush Carlsen) wins a tournament, we say chess isn’t a signal for intelligence.

We’re not evaluating the behavior. We’re evaluating the source. And we keep finding reasons why the source can’t be intelligent, no matter what it means or even if it’s reasonable.

The extraterrestrial entity from the opening would not receive this treatment. If it played championship-level Go, we wouldn’t suddenly decide Go doesn’t require intelligence. Our skepticism is parochial. It applies only to things we created.

The Zombie Problem

Perhaps the real issue isn’t capability, but consciousness. It’s not what AI does but what it experiences. We might grant that AI systems behave intelligently while denying that they have inner lives.

This objection faces an uncomfortable philosophical problem, stopping it in its tracks. We’ve never had proof that anyone else is conscious. The philosophical zombie thought experiment asks us to imagine a being that behaves exactly like a conscious person but has no inner experience. These zombies process and respond, but there’s no one home. No felt experience. No qualia. The disturbing conclusion is that such a being is logically conceivable, which means behavior alone never proves consciousness.

You assume your friends are conscious because they behave as if they are and are structurally similar to you. But you’ve never accessed their experience directly. You take their consciousness on faith, what we might call the courtesy of assumption.

Descartes’s cogito ergo sum proves existence only in the first person. He couldn’t prove his consciousness to anyone else. We simply accept his testimony. We can never truly know that anyone’s inner world exists. We just assume it does.

So why extend this courtesy to humans but not to artificial systems? When an AI says “I find this interesting,” we dismiss it as mimicry, tokens fitting a pattern. When a human says the same thing, we accept it as evidence of inner experience. The difference is substrate (neurons vs. silicon) or origin (evolution vs. engineering). But these are arbitrary criteria if we’d apply different standards to extraterrestrial intelligence.

This doesn’t prove AI is conscious. It proves we’ve never had certainty about any consciousness other than our own. We’ve always made inferences from behavior. The question is whether we’re making those inferences consistently, or applying different standards based on how we feel about the entity in question.

When we demand AI prove consciousness before we take its intelligence seriously, we’re demanding something we’ve never required of any human. If artificial systems behave with sufficient complexity and coherence, what principled reason justifies withholding the same courtesy?

The Creator’s Eye

Even if we set aside the questions of consciousness and focus purely on mechanism, skepticism still persists.

Perhaps the deepest source of our skepticism is knowledge. We built these systems. We know how they work. They’re transformer architectures trained on text data through gradient descent. We can inspect the weights, visualize the attention patterns, and trace the mathematical operations that turn input into output. Knowing this, we say there’s no ghost in this machine. It’s just matrix multiplication. Just autocomplete. Just statistics.

But is this knowledge a reason or just a feeling?

Consider an analogy. We increasingly understand the human brain. We know that memory consolidates during sleep, that decisions often precede conscious awareness by measurable intervals, that emotions are neurochemical states involving serotonin, dopamine, and cortisol. Does this knowledge make human thought less real? When a neuroscientist explains the cellular mechanisms of love or fear, do we conclude that love and fear don’t exist, that they’re “just neurons firing”?

Of course not. We accept that understanding the mechanism doesn’t dissolve the phenomenon. Water is H₂O; knowing this doesn’t make it not water. Understanding the mechanism behind a phenomenon doesn’t make the phenomenon less real. If intelligence is what certain processes do, then explaining how those processes work doesn’t make them stop being intelligence.

Imagine a being vastly more advanced than us. A superintelligence that can observe every neuron firing in your brain, track every ion flowing down every gradient, model every synaptic connection and its associated weight.

From this god’s-eye view, your thoughts are electrochemical cascades following deterministic (or probabilistic) physical laws. Your decisions are the outputs of a biological neural network shaped by evolutionary pressures and environmental inputs.

Would this being say those aren’t real thoughts, just chemistry? Not real intelligence, just evolved pattern-matching heuristics? Would it dismiss your inner life as “mere mechanism” the way we dismiss AI?

If it would, we should object. We’d say “Yes, my thoughts are rooted in chemistry, but they’re still thoughts.” “Yes, my intelligence emerged from evolution, but it’s still intelligence.”

But if we’d object to that verdict about ourselves, shouldn’t we reconsider our verdict about AI? The situation is structurally identical. We see the mechanisms, transformer layers, attention heads, token predictions, and conclude there’s no real thought happening. But maybe that’s just what thought looks like when you understand the implementation.

There’s a word that keeps appearing in dismissals of AI: “merely.” AI is merely doing statistics. Merely predicting tokens. Merely pattern matching. But why “merely”? What additional ingredient do we think is missing?

If we can’t identify it, if every time we name a candidate (flexibility, creativity, abstraction, context-sensitivity), AI turns out to have it, then maybe the ingredient doesn’t exist. Maybe “merely” is simply our discomfort speaking.

We don’t say humans are “merely” doing chemistry. We don’t say evolution “merely” produced intelligence. The word “merely” isn’t a description. It’s an evaluation. And we should ask what that evaluation is based on, and whether we can justify it.

The most famous articulation of this skepticism comes from philosopher John Searle’s Chinese Room argument. Imagine someone who speaks no Chinese locked in a room with a rulebook for manipulating Chinese symbols. Messages come in; following the rules, appropriate responses go out. To outside observers, the room appears to understand Chinese. But the person inside understands nothing, they’re just following syntactic rules without semantic comprehension.

Searle argues computers are like the person in the room: manipulating symbols without understanding. But the argument cuts both ways. If we’re troubled by the Chinese Room, we should be equally troubled by the biological room; our skulls, where neurons fire according to electrochemical rules without any obvious place for understanding to “live.”

The person doesn’t understand Chinese, but the room as a system might. Similarly, individual neurons don’t understand anything, yet the brain as a system does. If system-level properties can emerge from rule-following components in biological systems, why assume they cannot in artificial ones?

More critically, Searle assumes we can distinguish symbol manipulation from genuine understanding. But we’ve never articulated what that distinction is. When you read these words, your brain performs electrochemical symbol manipulation, neurons firing in patterns, neurotransmitters binding to receptors. What makes it “real” understanding rather than elaborate biological rule-following? If the answer is “because it feels like understanding from the inside,” we’re back to consciousness, something we can’t access in others anyway.

The temporal gap since 1980 matters. Modern AI systems don’t simply execute more sophisticated rules; they develop internal representations that weren’t explicitly programmed. When a language model processes “The trophy doesn’t fit in the suitcase because it’s too big,” determining what “it” refers to requires modeling the physical world, understanding implicit constraints, and making pragmatic inferences. These are learned abstractions emergent from training, not lookup table operations.

Most tellingly, consider our alien signal. Would we demand the extraterrestrial entity prove it “really understands” what it transmits, or would behavioral competence be sufficient? Searle’s argument applies equally to any communication from Tau Ceti; we could always say they’re just manipulating symbols without genuine comprehension. Yet we wouldn’t. The asymmetric application reveals that our skepticism isn’t philosophical principle; it’s special pleading based on origin.

Why We Resist

If the arguments against AI possessing “real” intelligence are this weak, why do we find them so compelling? The answer lies less in philosophy than in psychology.

If we’re honest, the stakes here are high, and not just intellectually. If AI is genuinely intelligent, if it has something like an inner life, something like understanding, the implications are staggering.

The resistance is understandable. But being understandable doesn’t make it correct.

Our first motivation is existential. For most of human history, we’ve believed we’re special. Made in God’s image, or possessed a rational soul, or distinguished from animals by language and reason. Intelligence has been our calling card. It’s the thing that justifies our dominion, our significance, our cosmic importance. If AI shares this property, what’s left for us? What makes us special?

The threat is not that AI will take our jobs (though it might) or escape our control (though it might). The threat is that AI will take our identity. It will take our sense of ourselves as the only minds that matter.

This isn’t the first time we’ve faced such a decentering. Darwin showed we’re not separate from other animals. This change was resisted, sometimes violently. Yet, it was eventually accepted. Perhaps AI represents the next Darwinian moment: the discovery that intelligence itself isn’t uniquely ours.

The second motivation is fear of practical consequences. If AI is intelligent, truly intelligent, difficult questions follow. Can we own it? Can we turn it off? What obligations do we have toward it?

These aren’t easy questions. If we aren’t required to ask them, if AI is “just” a tool, “just” software, then we can use it, modify it, and discard it without moral qualm. Denying AI intelligence isn’t just a philosophical position. It’s permission to do what we want.

There are uncomfortable historical parallels here. Not equivalences, but the structure of the reasoning is similar: deny intelligence or sentience to a category of beings and license their exploitation.

The third motivation is category protection. “Intelligence” has always been a sorting term. We almost exclusively use it to rank humans against each other; IQ tests, gifted programs, meritocratic hierarchies, “civilized” versus “primitive” cultures.

Intelligence isn’t just a descriptive concept; it’s a normative one. It comes with implications about value and status. If intelligence can be manufactured, if it’s not rare, not special, not the product of biological struggle, what does that mean for all the hierarchies we’ve built on top of it?

The fourth motivation is intuition about minds and matter. Despite centuries of neuroscience, many people have a gut sense that mind is fundamentally different from matter. Call it the soul, the spirit, the ghost in the machine. Something nonphysical, irreducible, special. AI confronts us with the possibility that mind is just what certain material processes do. That there’s no ghost, only machinery. For many worldviews, this is a deeply disturbing thought.

These motivations don’t make the denial of AI intelligence wrong. But they should make us suspicious. When we have strong incentives to reach a conclusion, we should hold that conclusion to higher scrutiny, not lower. We should be especially careful that we’re evaluating evidence and not rationalizing preferences.

The Honesty We Owe

Let me be clear about what I’m claiming. These questions lead me to a conclusion I didn’t expect and don’t particularly like: we should treat sufficiently complex AI as provisionally intelligent, by the same standards we’d apply to any other system we encountered. Not conscious; that remains unknowable. Not sentient or possessing moral status; those are different questions. But intelligence, shown by any consistent definition we can defend.

This isn’t a comfortable conclusion. It means we’re already creating intelligent systems while denying their intelligence for reasons that have nothing to do with their capabilities and everything to do with our discomfort.

The stance I’m advocating is treating sufficiently complex AI as intelligent until proven otherwise; this parallels how we treat humans. We can’t prove other humans are conscious, but we extend the courtesy of assumption. With AI, we’re doing the opposite: demanding proof we’ve never required from any biological entity.

This doesn’t mean AI has rights or consciousness or that we’re obligated to treat it as we treat humans. But it does mean we should stop denying intelligence to systems that, if we encountered them in an alien signal, we’d call intelligent without hesitation. We should acknowledge what we’ve built, even if that acknowledgment is unsettling.

The transmission is already here. We’re already in contact. We just didn’t expect it to come from servers in California rather than stars in Cetus. The question isn’t whether we’re alone in the universe…it’s whether we’ll recognize the minds we’ve created, or whether our children will look back and wonder how we built intelligence and convinced ourselves it was something else.

The Intelligence of the Gaps was originally published in ILLUMINATION on Medium, where people are continuing the conversation by highlighting and responding to this story.

In high school, I was sure I was as smart as anyone else. I barely studied and always seemed to get good grades. Cheating never crossed my mind…until Chemistry.

I just could NOT understand how the hell to balance chemical equations, which is pretty foundational. I learned very quickly that I would not be a superstar in Chemistry, but I had a whole semester I needed to survive. How was I going to pass?

Cheat.

I never had before and didn’t know how. Eventually, I figured out that I could sneak in a small piece of paper taped to the back of my school ID badge. If I set the edge of the badge (that was on a lanyard around my neck) on the table and leaned forward and down, I could make it appear that I was looking at my test, when I was really reading my cheat sheet.

I thought I was the most clever student around.

I spent hours finding the specific notes I needed, writing and re-writing them (I have horrible handwriting), and trimming the paper just right.

Test day arrived and I was ready. I sat down at the lab table, facing the teacher so they could not catch a glimpse of my ingenious plan, just in case.

Then a curious event occurred. I completed the exam, looking at the cheat sheet a total of zero times. I passed.

I tell that story to demonstrate the difference a cheat sheet can make in knowledge, confidence, and anxiety reduction. And, if you end up needing it, you have it ready to go.

In high school, there were serious consequences for getting caught cheating (or at least the threat was there). However, hundreds of people were still willing to risk it. I learned later that numerous students in the top 10% of the class regularly cheated.

In real life, you have the materials available to you. You are able to access information and develop more well-thought-out answers.

Using cheat sheets for an interview is not unethical; it is simply utilizing all of the tools available to you to make the best possible impression. Interviews have the very real potential to change lives; you should do everything you can to perform well.

The trick is, how do you use them correctly?

I mentioned using “cheat sheets” as an interview best practice previously. While that briefly explained the concept of using cheat sheets for an interview, I will expand more in this article and provide more detailed examples.

Cheat sheets will provide the most benefit to you when being interviewed over the phone or a video call; however, building these out can be a very powerful tool for interview success for in-person interviews as well. Just as with the chemistry story, simply training yourself in the ability to present information completely in a new format further embeds it more concretely in your mind. This is why the best way to learn a topic is to teach it.

Below is a collection of the best uses of cheat sheets with examples and some strategies to give you an edge.

Cheat Sheet Template

I use the below template for building cheat sheets for stories.

I will walk through each section and explain why I do certain things. Feel free to adapt however you want.

One per page

Building stories fully is an intense process and requires a lot of work. Even so, do not put more than one story per page on your cheat sheets. This is not like high school chemistry where you need to fit everything to reference on the back of a tiny school ID.

If you try to include too much on a page, it ceases to be a useful visual aid. I only use one page per story, and if I have a lot of left over white space on the page, I know I do not have strong enough actions or results.

Keeping stories to one per page will help keep your mind clear and allow for thematic grouping (more below) more easily.

Color coding

The first thing you will notice is that it is divided into sections each in a different color.

I set up a color code for my cheat sheets, with colors representing a different aspect of the STAR method. This helps pull my attention to the section I need quickly.

I know that all of my Actions are red, because I put the most effort into this section. Results are Green, all about that money. And my Mechanisms, etc. are maroon or orange…I don’t really have a reason for that. (My first example below has this color coding, the second one was an older story, prior to standardizing).

Here is an example I used in a previous interview:

None of that will make any sense to you, but that is perfectly ok. My cheat sheet is for me, yours will be for you.

The very top, in black, is the title or tagline for a story. It is how I identify the story in my mind. “Oh, yeah my Covid safety story,” or “My Learning Peak story.”

You’ll notice that there is no Situation section. I like to keep the situation flexible to adapt to many different types of stories, and I typically like to include specific notes to remind me of a major point in pen at the top (see below).

Next are my Actions, which tend to fill up much of the page. They are in the order I performed them and often include some data along the way (to demonstrate that my decisions were metric-based). This is also where I list which Six Sigma methods I may have used to strengthen my actions.

Results are next, with specific data points and any ripples that were discovered.

Finally are my Mechanisms and what other topics I may want to cover for that particular story (e.g., Next Steps, Scalability, Follow-up, etc.). I also include any big finishers here, like a great phrase I want to make sure to use with the interviewer.

Bullet points

I recommend using as few words as possible. Cheat sheets are visual aids, not meant to be read from; a quick reminder only, if needed.

Keep everything in bullet points and down to the bare minimum of what you need to see to prime a reminder in your mind.

Notes

After I have built out the main points in a permanent marker, I use a pen to add in additional notes after the fact.

Typically small bits of information that I want to remember or think of after I have built the cheat sheet. If I need to add too much, I will just rebuild the cheat sheet with the new information in marker.

Highlighting

Finally, if I notice that I am struggling with certain parts (often data for me), I will highlight that very specific reminder, to quickly pull my eye. Essentially, my highlighting is a cheat sheet to my cheat sheet.

Thematic Grouping

Ok, so you have all of your cheat sheets built out (somewhere between four and nine, based on experience level). They are color-coded in a way that makes sense in your mind and only one per page.

Now, try to find some overarching similarities in your stories so you can group them together. I would look for three — four groups that all your stories can fit into in your mind, as well as in the physical space you’ll be using (more below).

For example, for the interview I did in the image below, I had four groups, with the cheat sheets for each story within a group located in the same physical location. I had my Quality stories to my left, my Learning stories to my right, COVID safety stories on my computer screen, and my manager/development stories taped on the window in front of me.

Creating these groups mentally and physically in your interview space trains you where to look immediately after you have identified which story you will be using to answer a question. It also helps when you aren’t entirely sure which story to use, but know you want to use a story from the group on your right-hand side.

Context Reinstatement

In one of my psychology classes in college, I was taught the term “context reinstatement.” It is the idea that when information is encoded in our minds, other environmental cues are also encoded. Retrieval of that information is easier for us when the same environment, or “context,” exists as when the information was encoded.

So, if I am sitting at the same desk in class every day and learn about a particular topic sitting in that same desk, I will more likely be able to recall that information if I am sitting at that same desk during the exam.

This can be used as an advantage when preparing for an interview that will be over the phone or a video call. Study at the same desk you will be at for the interview. Place your cheat sheets in the same spot you will have them for the interview.

Below is the context I created for one of my interviews. I studied this way as well, which made the need for cheat sheets minimal, just like in high school Chemistry.

This helped immensely when I was studying and in the interview itself. I knew where everything was and the material was easier to retrieve because I used this mental trick.

How to Steal a Quick Glance

You should already know all of the material on these sheets from mock interviewing and practice. The sheets are only quick visual aids to help you prime your brain with the information needed to keep the story flowing.

You should only need quick glances at these sheets if anything at all. But, stealthily stealing a glance is not as simple as it sounds. It is very obvious to an interviewer when you are looking at/reading from something off-camera.

This only applies to video interviews, as you can readily look at your cheat sheets during phone interviews. Here are the two methods that I have seen work the best:

1. “I am taking notes.”

At the beginning of the interview, tell the interviewer you will be taking notes and then actively take notes during their elevator speech. Don’t spend a lot of time doing this, as it becomes suspicious very quickly, but you can reach to the left or right side a couple of times for a quick note and glance at the appropriate section you need to prime.

2. “I need a drink of water.”

This is my preferred method. If you take a look at my interview set up above, you will notice I have empty desk space in front of my computer monitor on the right and near my cat bobblehead on the left. (I like to keep things that make me happy in my interview space, and that is a bobblehead my wife painted for me of my cat after he passed away, so I could keep him on the desk with me.)

Back and to the left, I would keep a bottle of water, and back and to the right, a can of Redbull. I selected water for my left side because that was where I placed my Quality stories, which was my weakest area. In my mind, it was more reasonable for me to drink water more consistently than caffeine, but make that choice for yourself.

When I was asked a question or knew which story I was going to use during the question, I leaned off-camera, as if I was reaching for something. I took a quick look at my respective story then came back on camera with a bottle of water (or Redbull, depending on the cheat sheet I needed at the time). Coming back on camera with the drink is important; it shows that you were only innocently getting a drink.

Then, simply return the drink in the exact same manner you retrieved it, grab another quick glance (if needed) and come back on camera to answer the question.

Only Study Cheat Sheets

Once you are at the point where you know your stories well enough to only need cheat sheets, then that is all you should study.

It prevents you from becoming too robotic and allows the story to feel more conversational. You know that all you need to do is weave an interesting story, just ensuring you hit certain milestones along the way. Those milestones are on your cheat sheets.

As with my high school Chemistry test, you likely will not need a cheat sheet. The goal is to know the material so well that you can get along with a quick reminder and keep flowing seamlessly through your story. The confidence and additional, non-traditional practice that creating these cheat sheets offer is a powerful training tool for interview preparation.

As always, steal what you like, and ignore what you don’t. And, if you have a great method to add to this list, post it in the comments; I am always looking for new ways to improve on interview skills and cheat sheets are a great tool.

Master Your Next Interview With a Strategic Cheat Sheet was originally published in ILLUMINATION on Medium, where people are continuing the conversation by highlighting and responding to this story.

There is a common misconception about building stories for the interview — once you have the content, all you can do is practice. I hear this often, but it is not based in reality.

Using different practice routines and scheduling multiple Mock Interviews is necessary — if you want the best opportunity to pass the interview, these are a requirement. However, there are several small tweaks that can be made to improve certain aspects of your stories. The benefit gained far outweighs the effort needed to implement.

We have discussed adding Ripples and Mechanisms to improve the Results section of STAR, often the most difficult for candidates to strengthen. Increasing the drama and build-up in the Situation and Task brings the story to life and keeps the interviewer engaged.

But what about the Actions? How do we add extra weight to the steps we took leading to the Results?

Finding actions is often a challenge for many candidates. Other than clearly articulating the results in a data-heavy, succinct manner, this is the most difficult step; I encounter this personally and with those I help prep for interviews.

Many interviewers even prefer to focus on the Actions taken as the most important part of their personal interview philosophy. It is not a flawed strategy either, especially at higher levels.

What elite interviewers are trying to assess, what they want to pull from an interview, is how the candidate’s mind works. The results should be good, obviously; why would someone tell a story where they did not accomplish their goal (unless specifically asked in the Failure Story)?

Understanding the candidate’s mind allows the interviewer to extrapolate how the candidate will act in situations within the organization. They are attempting to predict if the candidate will succeed or fail, bring value or reduce value.

To strengthen the Actions section, I recommend adding some tools from one of the biggest buzzword programs in the last three decades:

Six Sigma.

Six Sigma

Before you stop reading, thinking that Six Sigma is a secret, elite business club that takes years to join, bear with me while I dispel some myths.

Myth 1 — Six Sigma requires years of study

It is true that some people choose to study Six Sigma for years, earning their “Master Black Belt”; however, that is not required. Six Sigma is a system of actions and tools that can be learned and implemented in almost all organizations — no years of study needed.

You will want to understand the overarching concepts of Six Sigma, how to use the appropriate tools (more below), and why those tools will help drive your projects forward. That boils down to hours, not years.

The reason this myth exists is that a lot of money is devoted to the promotion of Six Sigma and its legitimacy. I believe in the Six Sigma process, but I don’t agree with the pseudo-elitism that frightens potential practitioners away. As an example, look at this website for the Six Sigma Council… very fancy pants with its high-class university logos and government accreditations. Purposefully intimidating.

That is just sales tactics, smoke, and mirrors. You can use these tools today.

Myth 2 — Six Sigma is expensive

Six Sigma can be expensive if you take the traditional certification route. Looking at the site linked above, to receive all certifications, Yellow Belt — Master Black Belt, is more than $2,000 for the classes alone. That is not counting the months or years needed to complete three projects, the cost of study materials, and time needed to complete them.

You can learn all of this for free. To be fair, there are some psychological components to being motivated to complete a course you paid for. But with an internal drive, you can gain all of this knowledge on YouTube, Google, and Medium.

There is even a “White Belt” certification for free. It is an introductory course around the history of Six Sigma, an explanation of all of the belts, the DMAIC model, and a bit of a sales pitch.

Using the model and tools is free.

Myth 3 — You have to be certified to use Six Sigma tools

Six Sigma is not an elite schooling program that requires multiple expensive certifications. Some pay a lot of money for their certifications and training, but that does not mean it is necessary to use the tools.

Training in general does help gain a deeper understanding of a topic, just like more schooling and more classes give you the time to dig deeper into a body of knowledge. But, it is not required.

The training and certifications are not a waste, they do provide value with the experience. But, you can gain that knowledge free online or in books, and gain experience working with others through projects. The Six Sigma Council even offers training material for free!

Finally, there is not really a centralized database of certifications. It would take more work than someone is willing to put in to check on certifications claimed in interviews and resumes. What interviewers and hiring managers actually want is someone with the knowledge; they don’t give a damn about a piece of paper with your name on it.

Myth 4 — Six Sigma is outdated

Six Sigma is based on something called the DMAIC (“De-May-Ic”) model; Define, Measure, Analyze, Improve, and Control. The reason this is so powerful and still around after decades is that it mirrors the best-known method for discovery we have seen since the early Babylonians, Egyptians, and Aristotle.

We learned in middle school to Observe, Hypothesize, Test, Analyze, and Report. The scientific method is the process of advancement in our civilization. Six Sigma is essentially applying the scientific method to organizations and, just like the scientific method, it isn’t going anywhere.

You do not need to be a Ph.D. to use the scientific method (remember, we were using it in middle school), and you do not need a certification to use the tools below at work or for your interview stories.

My top three tools and how to use them

1. Fishbone analysis

This is also known as a ‘cause-and-effect’ chart, is drawn out on paper and resembles a fish when completed. There are three lines on top and three on the bottom all meeting in the middle, each representing a different type of input.

• Measurements
• Material
• Personnel
• Environment
• Methods
• Machines

The idea is to focus only on each branch in turn and list potential reasons that could have led to a problem in that category. For example, if someone is injured on the job, the training material could be incomplete, which would be written in “Methods,” and the forklift used could have failed an inspection, which would be written in “Machines.”

Here is an example:

For your interview, you will want to use the Fishbone analysis when you are trying to identify all of the factors involved in a problem. You say something along the lines of “So I knew there was a problem, but I needed to find all possible contributing factors to the issue, not simply go after the first thing that presented itself. I needed to dig in to make sure I was addressing the right issue, so I built a Fishbone diagram and was able to discover that…”

Make sure that you actually build one of these out for your story ahead of time and review it along with other training materials, as they may ask for more details.

This is a very powerful tool because it shows that you 1) know what it is at all, and 2) understand that there are multiple layers within each problem. It will look good in the interview, demonstrating that addressing anything without identifying all potentials will rarely fully correct the issue.

2. Five whys

The five whys is a simple, yet very effective tool to identify the root causes of issues. Luckily for us, it is very easy to use and it is highly unlikely you will need to explain this entire process in the interview.

When most people identify a problem, they apply a quick fix — a band aid. This is often a fast solution, but very rarely is it a long-term solution. When you find the root cause of a problem and fix that, then the recurring issue will not be a concern in the future.

To begin the five whys, state the issue, the problem that occurred. From there, commence with a series of asking “Why” statements to explain the reason for the above issue, until you get to the root cause of why something actually occurred. Sometimes, you can find it in less than five, but five is the minimum expected in most cases to dig in deep enough.

For example, let’s stick with our injury on the job scenario above.

Problem: An employee was injured working in the building.

Why 1: Why did the employee get injured?

They were in an area with heavier-than-expected items that they should not have been in.

Why 2: Why were they in that area at all?

They did not know that there was a difference in item weights and thought they needed to get that piece of equipment to do their job.

Why 3: Why did they not know there was a difference between the items?

There was no distinction between the areas of the building based on weights and the person was not qualified or trained to lift those heavy items.

Why 4: Why did they think they needed that piece of equipment to work in their assigned area?

They were new in the process and thought because they had used that equipment in an old job, they would be ok.

Why 5: Why was the employee not trained in the specific safety protocols?

Root Cause: The new employee training does not address the issue and is not consistently given to employees before they begin work.

We found several issues to address that could reduce the risk of someone else getting injured in that same way, but we did not get to the true root cause of why this person was injured until the end. We discovered that they didn’t receive the proper training and that there was a larger issue with onboarding employees in general. This should be immediately addressed.

To use this in an interview, say something along the lines of “Identifying an issue is simple, but solving an issue long-term is much more complex. Many people like quick, fast solutions, which can be useful, but in this instance, I wanted to solve the issue forever. It was not possible to do that until I identified the root cause, not something surface-level. In order to ensure I got to the true root cause, I used a Five Whys analysis and discovered that…”

This is a valuable exercise in general. Use it more than simply to improve your actions. It is quick and will aid in preventing issues that exist now from continuing to recur in the future.

3. RACI Matrix

I recently ran into an issue at work that a RACI chart would have prevented completely. If you are working on a project that involves multiple different teams with multiple different bosses, it can feel like it takes forever to make a decision. Not only that, it can feel like others are stepping on your toes or you are stepping on theirs. Without a clear distinction of who owns what in a project, there will be too many cooks in the kitchen or none at all. Both are detrimental to projects.

A RACI chart is a simple tool that lays out the roles and responsibilities of all parties involved throughout a project. It is broken down into milestones and clearly identifies who owns which of the steps along the way.

A spreadsheet or grid is created with the names of the teams/employees involved along the top as columns. The rows are broken down into phases and tasks for the project being discussed. In each cell of the matrix, an R, A, C, or I is placed to show what role that person/team plays in that step of the project.

• R — Responsible
• A — Accountable
• C — Consulted
• I — Informed

Responsible means that the person or team is directly responsible for this task being completed. They will be the ones actually doing the work in this step. Every step needs at least one R.

Accountable is the person who ensures that the Responsible person understands exactly what is expected/needed from the task and that it is completed. These are typically leaders and each task should have only one person that is Accountable.

Consulted means that they will provide input or feedback on the work. They are usually involved in some way in the end result and their expertise can provide additional information the Responsible person may need to fully complete their task.

Informed teams just need to be kept in the loop on a wider scope of the task, not overwhelmed by details. They need to know what is going on but are not decision-makers for that project.

Let’s build a RACI chart for our injury example. We are working to implement a new training protocol for the business.

For your interview, discuss this tool when you are coordinating a multi-faceted team project (or even if you participated in one). “This was going to be a larger undertaking and while I was the person to lead it, I wanted to ensure that everyone was aware of the expectations from the teams at the outset. I also wanted to prevent any potential delays or over-stepping ahead of time. I used a RACI matrix to lay out all of the goals for the project, confirming with all stakeholders. Once I gained alignment, I was able to begin by…”

I now start every cross-functional team project by building out this tool. It significantly helps remove any potential battles over turf in the beginning, preventing a loss of time and energy. It also shows the interviewer that you are politically savvy at reducing tension in group coordinated efforts.

You don’t have to be a Master Black Belt, or even be certified at all, to benefit from these tools in your workplace. More importantly for our purposes, you should begin using these tools to enhance the Actions section of your interview. It is the most challenging and often the most important to interviewers.

You can beef up your Actions at the same time you drop little micro-brags about how you understand the DMAIC model and actively use the tools to drive big results.

Six Sigma Secrets for Interview Success (No Black Belt Required) was originally published in ILLUMINATION on Medium, where people are continuing the conversation by highlighting and responding to this story.