I have an old toy robot in my office that my kids played with when they were little. Its name is E.M.I.G.L.I.O.
Even though it is a toy, this Italian-made robot was interesting technology when it came out. It was remote-controlled; the remote had a microphone that turned my voice into a robot’s, and it had a tray sturdy enough to deliver a video game (or some other surprise) for my kids when they visited the office.
Looking back, it’s barely even technology, let alone a robot. But that’s because I’m evaluating it based on what’s possible now.
I feel the same when I think about my previous company, IntellAgent Control, and what we considered AI in the 1990s. We made a sales automation solution for teams before tools like Salesforce existed. At the time, the decision logic we used was innovative. The premise is still valid today, but the technology and implementation scream “relic of a time gone by.”
As another aside … when I searched for Emiglio (in order to write this article), I was astonished by the archive of old robots someone had put together. The site is like a specialized Wikipedia site for toy robots. Each entry includes high-quality photos of the robots and their packaging. It also includes facts, marketing copy, ads, and patents.
It got me thinking about how much of history (and esoteric knowledge) only exists because a tiny community of people decided it needed to be cataloged or preserved.
“Garbage In, Garbage Out. Nothing In, Nothing Out.” What are we missing from the past because history is often written by the winner (or because no one volunteered to chronicle what happened)?
Even a site like Wikipedia has some serious content curation issues. For example, the top 50 Wikipedia editors have each contributed more than 500,000 edits. Think how much is missing.
We often worry that AI will change the future. I’m starting to think its bigger impact may be on the past. Every archive, database, and knowledge repository becomes part of the training data for how future generations understand the world. The stories that get preserved gain influence. The stories that don’t slowly fade from memory.
History has always been written by the people who showed up to record it. The only thing changing is the scale.
The future won’t just be shaped by the data we create; it will be shaped by the data we preserve. Made more personal … If you don’t intentionally preserve your data and stories, your company may literally not exist in the models that future decision-makers rely on.
The real AI question isn’t what it’s becoming. It’s what you’ve already handed it — and whether it earned the keys.
Years ago, a programmer brought me his work, and I told him it was wrong. He pushed back and reminded me that he was the one with the computer science and math degrees (and I’d only been running an AI company for thirty years). He confidently declared that he’d done the math right.
He had … but the answer was still wrong. Yes, he’d done the calculation perfectly; that was the easy part. The hidden wisdom lay in a distinction he didn’t consider … whether he used the right equation and data.
I think about that moment often now, because today’s AI has the exact quality that made his mistake so hard to catch: it’s fluent, polished, and supremely confident. And confidence is precisely what makes us stop checking.
The Quiet Handoff
Most leaders are busy debating what AI is — conscious, AGI, a partner, a threat, or even a deity. It’s an interesting debate. It’s also largely beside the point. The question that will actually shape your business is quieter: what authority have you already handed it?
Authority doesn’t transfer in one dramatic decision. It migrates. A system starts by drafting, then recommending, then deciding — and somewhere in there it stops being a tool you use and becomes the place the decision actually gets made. In our world, that isn’t one algorithm you can watch; it’s thousands, far more than any human can track in real time. You don’t notice the handoff. You notice the result.
Would You Promote It This Fast?
So I use what I call an AI Maturity Model, and it’s the same standard I’d apply to a person before trusting them with real responsibility. I want to see the process, not the answers. Does it look at and consider the right things? Can it tell good data from stale, dirty, or incomplete data (because bad data compounds into worse results)? Can it turn information into knowledge — first by bringing order to chaos, and then by making the finer distinctions that separate wisdom from raw horsepower? Can it rank and evaluate options, and does the ranking still hold when I change the goal? Can it make a real recommendation under pressure, and not just hand me what’s at the top of a list?
Here’s a simple graphic of the maturity model’s ascent.
Only after a system shows me all of that — and shows me it’s still getting better — does it earn the right to act on its own.
Maturity isn’t whether the AI sounds sure of itself. It’s whether you granted autonomy at a rung it actually climbed, or one you handed over on confidence alone. Most over-delegation is exactly that: trusting the answer because it was stated well.
At scale, you can’t supervise your way to safety — there’s simply too much happening. So you do two things. You build sensors and feedback loops that flag when something drifts off track. And — this is the part most people skip — you plant failures on purpose. You feed the system things you know are wrong and confirm it catches them. Because if it misses the faults you buried, you can’t assume the rest is fine. Assume the opposite … a lot more is slipping through.
And when something is wrong, I audit the same three places I always have.
Long before AI, I knew that bad results traced back to people, processes, or data (or worse, a combination of those things). That hasn’t changed — except now “people” might be an agent, a swarm of them, or an orchestrated pipeline.
Even though the doer might have gone digital, owning the answer didn’t.
AI 101: When Not To Grab the Wheel
Here’s the part that took me longest to learn — and I’ll admit I learned it the hard way, in a business where a bad impulse costs real money. Once you’ve handed a system authority, the instinct is to reserve the right to grab the wheel the moment you get nervous. That instinct is usually wrong. The moment you most want to intervene is often the exact moment your intervention likely does the most damage (because what you’re bringing to it is fear, greed, and discretionary risk — the very things you built the system to remove).
The mature move isn’t to override on impulse. It’s to give yourself and your people enough visibility to stay oriented — to understand what’s happening and trust the direction (so you don’t panic and yank control at the worst possible time).
Human-in-the-loop is valuable, but it has to be organized, accounted for, and built into the framework (not simply the result of an emotional reaction).
Years ago, I built something I call “filtered relevance” around a simple fact: people can really only remember about seven things at once (think about a phone number, if you know the area code). The point was to show someone exactly where they were in a process and the few choices that actually mattered, so they stayed in genuine command instead of drowning. It’s the difference between falling off a cliff while trapped in a cardboard box and piloting a helicopter. Nobody governs well from inside a box with no visibility, agency, or control.
So the real maturity test of this era isn’t whether your machines are ready for more authority. It’s whether you are.
Do that well, and AI doesn’t drain you — it frees you up and gives you energy and momentum.
If using AI or automation leaves you exhausted, you’re probably guarding the wrong things.
Done right, delegation isn’t about replacing your judgment. It’s about clearing away everything that isn’t your judgment … giving you the space and resources to focus on what you want to happen.
So before you ask what AI is going to become, ask the questions that actually decide it: What have you already handed over? Did it earn that authority (or did you grant it on confidence)? And when it’s wrong, will you know where to look to set things right?
The problem with history is that it rarely tells the whole story.
For most of history, the winners have written the history books. As a result, history has changed based on who’s writing the books, and in what country.
With AI getting more powerful, I fear that history will become even more subjective as it becomes easier to manipulate.
Ideally, history would be presented objectively, recounting facts without the influence of societal bias, the victor’s perspective, or the storyteller’s slant. But achieving this is harder than it seems, even before technology.
Think about your daily life – it is filled with many seemingly innocuous judgments about your perception of the economy, what’s happening in the markets, who is a hero, who deserves punishment, and whether an action is “Just” or “Wrong”.
I’m often surprised by how frequently intelligent people violently disagree on issues that seem clear-cut to them.
Even though most people would agree that genuinely understanding history requires a clear, unbiased picture … I think it’s apparent that history (as we know it) is subjective. The narrative shifts to support the needs of the society reporting it.
The Cold War is a great example in which the interpretation of its causes and events has changed: during the war, immediately after the war, and today.
But while that’s one example, to a certain degree, we can see it everywhere. We can even see it in the way events are reported today. News stations color the story based on whether they’re red or blue, and the internet is quick to jump on a bandwagon even if the information is hearsay.
Now, what happens when you can literally rewrite history?
“Every record has been destroyed or falsified, every book rewritten, every picture has been repainted, every statue and street building has been renamed, every date has been altered. And the process is continuing day by day and minute by minute. History has stopped.“ – Orwell, 1984
That’s one of the very real risks of deepfake technology. As it gets better, creating “supporting evidence” becomes easier for whatever narrative a government or other entity is trying to make real.
There are so many news stories about people falling for AI videos and deepfakes that it’s hard to even pick one.
Is The Moon Landing Even Real?!
On July 20, 1969, Neil Armstrong and Buzz Aldrin landed safely on the moon. They then returned to Earth safely as well.
MIT recently created a deepfake of a speech that Nixon’s speechwriter, William Safire, wrote during the Apollo 11 mission in case of disaster. The whole video is worth watching, but the speech starts around 4:20.
Can you imagine the real-world ripples that would have occurred if the astronauts died on that journey (or if people genuinely believed they did)? Here is a quote from the press response the Nixon-era government prepared in case of that disaster.
“Fate has ordained that the men who went to the moon to explore in peace will stay on the moon to rest in peace.” – Nixon’s Apollo 11 Disaster Speech
Today, alternative histories are becoming some people’s realities. Why? Media disinformation is the cause and is more dangerous than ever.
Alternative history can only be called that when it’s distinguishable from the truth, and unfortunately, we’re prone to seeking information that already fits our biases.
We also have to increasingly consider the impacts of technology on art, music, science, and even history.
Actions have consequences, and powerful verification and detection capabilities are evolving (e.g.,open-source verification communities, forensic tools, and AI designed to detect forgeries).
As deepfakes and other manipulations get better, we’ll also get better at detecting them, but it’s a cat-and-mouse game with no end in sight.
The Power of Doubt
In 1983, Stanislav Petrov saved the world. Petrov was the duty officer at the command center for a Russian nuclear early-warning system when the system reported that a missile had been launched from the U.S., followed by up to five more. Petrov judged the reports to be a false alarm and didn’t authorize retaliation (and a potential nuclear WWIII where countless would have died).
But messaging is now getting more convincing. It’s harder to tell real from fake. What happens when a world leader has a convincing enough deepfake with a convincing enough threat to another country? Will people have the wherewithal to double-check? What about when they’re buffeted by these messages constantly and from every direction?
As we increasingly use AI for writing and editing, there is a growing risk of subtle changes being made to messages and communications without you noticing. This widespread opportunity to manipulate information amplifies these technologies’ capacity to influence people’s perceptions. As a result, we must be increasingly cautious about how the data we rely on may be altered, which could ultimately affect our perceptions and decisions.
Every day, I get even more excited about the new potentials and results of AI. I feel like a broken record because every month, there’s some new breakthrough that brings out the tech nerd in me.
But, as always, in search of the good (or better), we have to acknowledge and be prepared for the bad.
The practical implication is this: the information you rely on to make decisions — about markets, about people, about events — is increasingly vulnerable to manipulation that is indistinguishable from the real thing. Your edge isn’t just in what you know. It’s in how carefully you’ve verified it, and how diverse and independent your sources are.
You might believe you won’t be fooled or that you’re immune. However, even if you think so, we’re only as strong as our weakest link … and I assure you, there are some weak links.
Stay diligent! Stay engaged. And, as always … Onwards!
Though the phrase “once in a blue moon” suggests an incredibly rare event, blue moons actually occur every two or three years.
The name is a bit misleading, too. The moon won’t appear blue.
A blue moon is simply the second full moon in a single calendar month. Most years have 12 full moons, but every few years there’s a 13th — creating a blue moon somewhere on the calendar.
Rare enough to be interesting. Common enough that you won’t have to wait a lifetime for the next one.
It’s almost motivating. What if your “once in a lifetime opportunity” came every 2-3 years?
You approve a plan that assumes “average” revenue growth, “average” volatility, and an “average” customer. Then reality shows up: a cluster of bad quarters, a tail‑risk event, a segment that behaves nothing like the model. The problem wasn’t bad luck; it was the flaw of averages — the comforting but dangerous belief that things will naturally “even out” over time.
When you manage money or run a business, trusting that “things will even out” is not a risk-management strategy; it’s a cognitive trap.
This piece is about why the “law of averages” quietly sabotages gamblers, investors, and leaders, and how to design decisions that work in the real world of streaks, outliers, and fat tails.
Our brains are wired to find patterns, even in random events. This tendency (known as apophenia) can lead us to see connections where none exist.
The Misleading Law of Averages
It’s this very tendency that fuels the misconception of the law of averages. We expect randomness to “even out” because we see patterns in short sequences. This can be tempting to believe, especially when dealing with chance events.
Take a coin flip. After five heads in a row, it feels like tails are “due.” But the odds on the next flip are still 50/50. The coin doesn’t remember, and there is no invisible force pushing results back toward balance. That feeling of “due” is your brain’s pattern‑machine misfiring (seeing order where there is none).
The same misfire shows up when you notice your “lucky” number all day, or when you assume a losing streak in markets must soon reverse. The world hasn’t changed; your perception has.
While there are some reasonable mathematical uses of the law of averages, in everyday life, this “law” mostly amounts to wishful thinking (which can lead to dangerous actions).
This natural desire for order and predictability can lead us astray when dealing with chance events.
Why is it Flawed?
The law of averages often leads to a misconception called the gambler’s fallacy. This fallacy is the belief that random events can somehow “correct” themselves to reach an average. In reality, every coin flip, roll of the dice, or spin of the roulette wheel is a fresh start with its own discrete probabilities. The odds remain the same no matter how long the losing streak persists.
Leaders, executives, and investors commit the same error when they build portfolios, forecasts, compensation plans, and product roadmaps around “average” conditions. In reality, performance lives in the extremes — streaks, clusters, and outliers that the average politely hides.
In practice, that looks like:
Approving a sales plan that “works on average” but can’t survive two bad quarters in a row.
Pricing a product for the “average customer” and missing the segments that actually drive profit.
Building a portfolio that’s fine in normal markets but breaks under clustered volatility.
A better approach is to design for ranges — asking “What happens if we get three standard deviations of bad luck?” and “What’s the cost if we’re wrong?” — instead of optimizing for a single point estimate.
It’s a good reminder that ‘facts’ can lie, and assumptions and interpretations are dangerous. It’s why I prefer taking decisive action on something known, rather than taking tentative actions about something guessed.
Below is a video about why we underestimate risk in the face of uncertainty. It discusses the “seven deadly sins” of averages and how a greater understanding of these flaws could prevent future financial meltdowns and other problems.
It’s important to distinguish the law of averages from the law of large numbers, a well-established statistical principle. The law of large numbers states that as the number of random events increases, the average outcome approaches the expected value. This applies in situations where many trials happen, and while past results of individual events are independent, the law describes the behavior of averages over a large number of trials. For instance, the average weight of a large sample of apples will likely be close to the expected average weight of an apple, even if some individual apples are heavier or lighter than expected. That’s why casinos make money in the long run.
However, in everyday situations (with a limited number of events), the law of averages is generally not a helpful way to think about chance or probabilities. The mistake is smuggling that long‑run logic into short‑run decisions. Your next coin flip, your next quarter, your next product launch is not “owed” anything by the past.
Understanding these misconceptions can help us make better decisions and avoid false expectations based on flawed reasoning.
Psychological Reasons Behind the Belief
Human decision‑making is riddled with biases. As discussed, our pattern‑seeking brains latch onto streaks, and the representativeness heuristic makes us assume that small samples (e.g., last quarter’s returns, last month’s pipeline, a handful of customer anecdotes) must reflect the whole.
Emotional factors also play a role. Our desire for control and fairness encourages comforting stories that “things will even out,” which is why investors double down after a losing streak and leaders assume a bad quarter will automatically be offset by a good one.
Additionally, social influences can reinforce these beliefs. Stories and anecdotes about streaks ending or luck changing often circulate among friends and family, further embedding the misconception into our collective consciousness.
Understanding these psychological reasons helps explain why the law of averages persists despite its flaws. Recognizing these biases can empower us to think more critically about probability and chance events.
Improving Decision-Making in Gambling and Investing
Recognizing the fallacy of the law of averages can significantly enhance decision-making, particularly in gambling and investing. Understanding that each event is independent can help participants make more rational choices. Instead of chasing losses in the hope that a win is “due,” savvy speculators understand that their odds remain constant and may choose to walk away or set strict limits on their betting.
In investing, this knowledge is equally crucial. Many factors influence markets. Nonetheless, believing that a stock “must” rebound after a series of declines too often leads to poor investment decisions. Investors who grasp that past performance does not dictate future results are better equipped to evaluate investments based on fundamentals rather than emotions or flawed expectations.
By dispelling these misconceptions, you can approach gambling or investing with a clearer mindset, reducing the risk of substantial losses driven by erroneous beliefs about probability and chance.
The world will never be as orderly as our brains want it to be. But if you stop designing for the average and start designing for reality, you’ll make fewer avoidable mistakes—and leave less of your future to chance.
You can also eliminate fear, greed, and discretionary mistakes by relying on algorithms to calculate real-time expectancy scores and take the road less stupid. Take a different kind of chance.
Just ask our AI Overlords; they’ll tell you what to expect!
It helps to have a map before you’re lost in the woods. It also helps to have one to anticipate the technological challenges, roles, and milestones that will shape your business’s future.
This week, I want to show you how to use ‘desire paths’ and “functional mapping” to build products and platforms that follow the path of least resistance for your customers and your team.
The Road More Traveled …
First, let’s examine a concept in design and transportation called Desire Paths. It refers to the path users take rather than the one intended by the builder.
In business and technology, the same thing happens. Customers, employees, and even markets rarely follow the path you draw on the whiteboard — they follow the path that feels most natural and useful to them. The leaders who notice and design around these ‘desire paths’ create products, processes, and strategies that are easier to adopt and harder to disrupt.
Think about your last internal tool rollout. Did people embrace the official workflow, or did they hack together spreadsheets, Slack messages, and side processes that actually got the work done? Those hacks are desire paths. They reveal where your real product or process needs to go.
Every business knows your product isn’t finished until the users have broken it, found new use cases, and pointed out bugs countless times.
If you are interested, there is an active online community forum that shares examples of Desire Paths. It may give you some ideas and laughs.
I am a creature of habit, and even though much of what I think, feel, or do seems to be happening based on real-time choices or decisions, much of that is just a well-worn rut of unconscious behavior.
As a subtle reminder to my son, who recently got married, I told him to expect many of his existing desire paths to change (even if he doesn’t want them to). The same is true for your company: big changes in context overwrite old paths, no matter how comfortable they feel. The question is whether you notice the new paths and design around them — or cling to the old map.
The lesson … It’s often easier to account for or take advantage of human nature (or nature) than to fight against it.
Building A Better Roadmap …
Here is a short video on how this relates to your business and tech adoption. I call it Functional Mapping. Check it out.
The video provides additional depth and detail beyond what’s covered in this post. I encourage you to watch it for a more complete perspective.
Functional Mapping is a way to visualize who does what, when, and why throughout the journey from thought to thing. It forces you to match roles and personalities to the specific phase of the journey.
Understanding the natural paths of technological development and human nature makes it easier to anticipate the capabilities, constraints, and milestones that likely will define your path forward.
That means understanding the different types of users and what they expect to do or accomplish.
Below is a diagram we use at Capitalogix to help us anticipate the roles (including their personality, tendencies, and skills) needed to navigate the milestones along our journey. For example, the person who imagines a product often loves ambiguity and possibility, while the person who builds it prefers precision and proof. And that knowledge helps you choose the person for the role, as well as the materials and resources you provide them with.
If you treat them as interchangeable, you create friction. If you map their functions clearly, you create flow.
While it’s easy to pay attention to what changes often, it’s also important to understand what doesn’t change. As long as people are building things for people, it’s crucial to recognize that both creation and adoption are heavily influenced by human nature (which isn’t likely to change).
Understanding this helps you anticipate and navigate the strengths, weaknesses, opportunities, and threats you will likely find on your path.
You’ve probably heard me talk about how Capabilities become Prototypes. Then, Prototypes become Products. And, ultimately, Products become Platforms.
Here is a simple example. Let’s describe a new AI model as a capability. When you wrap it into a simple internal tool, it becomes a prototype. Once it consistently solves a valuable problem for someone, you can turn it into a product they can buy. If that product becomes central to how customers run their business, it evolves into a platform that other products and services plug into.
The point is that the model is fractal. That means it works on many levels of magnification or iteration.
What first looks like a product is later seen as a prototype for something bigger.
SpaceX’s goal to get to Mars feels like their North Star right now … but once it’s achieved, it becomes the foundation for new goals.
This Framework helps you validate capabilities before sinking resources into them.
It helps you anticipate which potential outcomes you want to accelerate. It really means beginning with the end in mind. So, rather than simply figuring out the easiest next step, you have to figure out which path is most likely to lead to your desired outcome.
Pick one area of your business where people already ignore the ‘paved path’ and follow their own route. This week, map that desire path, identify which capability it represents, and ask what it would take to turn it into a prototype or product instead of fighting it.
The world is changing fast! Hope you’re riding the wave instead of getting caught in the riptide!
In 2016, I received this e-mail from my oldest son.
Date: Saturday, October 22, 2016 at 7:09 PM Subject: FYI: Security Stuff
FYI – I just got an alert that my email address and my Gmail password were available to be purchased online.
I only use that password for my email, and I have 2-factor enabled, so I’m fine. Though this is further proof that just about everything is hacked and available online.
If you don’t have two-factor enabled on your accounts, you really need to do it.
Since then, security has only become a bigger issue. I wrote about the Equifax event, but there are countless examples of similar events (and yes, I mean countless).
When people think of hacking, they often think of a Distributed Denial of Service (DDOS) attack or the media representation of people breaking into your system in a heist.
In reality, the greatest weakness is people; it’s you … the user. It’s the user who turns off automatic patch updating. It’s the user who uses thumb drives. It’s the user who reuses the same passwords. It’s the user who falls for social engineering. Each of those choices may seem like a mistake, but they also represent some hacker’s favorite pattern to exploit.
Whether it’s malicious or unintentional, humans are often the biggest security weakness.
It’s impossible to protect yourself completely, but there are many simple things you can likely do better.
Use better passwords … Even better, don’t know them. You can’t disclose what you don’t know. Instead, use a password manager like LastPass or 1Password, which can also suggest complex passwords for you.
Check if any of your information has been stolen via a website like HaveIBeenPwned or F-Secure.
Keep all of your software up to date (to avoid extra vulnerabilities).
Don’t use public Wi-Fi if you can help it (and use a trustworthy VPN if you can’t).
Don’t put information into GPTs that you want to keep private.
Have a firewall on your computer and a backup of all your important data.
Never share your personal information on an e-mail or a call that you did not initiate – if they legitimately need your information, you can call them back.
Don’t trust strangers on the internet (no, a Nigerian Prince does not want to send you money).
How many cybersecurity measures you take comes down to two simple questions … First, how much pain and hassle are you willing to deal with to protect your data? And second, how much pain is a hacker willing to go through to get to your data?
It doesn’t make sense to put all your data in a lockbox computer that never connects to a network … Nevertheless, it might be worth going to that extreme for some of your data.
Think about what the data is worth to you, or someone else, and protect it accordingly.
My son reminds, “You’ve already been hacked … the important question is whether you’ve been targeted?” Something to think about!
