Pedro Tiago

Introduction

At the core of human existence lies a simple truth: energy is our ultimate resource. Whether physical, mental, or emotional, every aspect of life demands the expenditure of energy. To achieve our desired goals, we must channel this energy into deliberate actions over time. These actions follow the principle of causality—each action leads to a reaction, and these reactions compound to create outcomes. By understanding this principle, we can optimize how we use our energy to reach our goals more efficiently.

Energy and Action

A goal is an effect we wish to bring about in reality, and like any effect, it has a cause. To achieve any goal, we must perform specific actions that serve as the cause. These actions require us to invest energy over a period of time. The more energy we allocate toward focused, intentional actions, the faster we can achieve our objectives. Furthermore, the quality of our actions depends on the skills we possess; the more skilled we are in executing these actions, the more efficient and effective our efforts become.

Maximizing the value of our energy investment involves focusing on actions that yield asymmetric returns—those that produce disproportionately large results compared to the effort expended. This requires an understanding of second-order consequences—the long-term ripple effects of our decisions and actions—to ensure we make choices that lead to sustainable success.

The Role of Beliefs in Action

To perform the right actions, we must first align our beliefs with our goals. Beliefs are shaped by our experiences, which we interpret through the lens of our emotions. Over time, these interpretations solidify into a belief system that influences our identity and, by extension, our behavior. However, if our beliefs are not aligned with the reality of our goals, they can limit us. Therefore, we must constantly reassess our beliefs, questioning their validity and adjusting them based on reasoning and learning from others’ experiences. By reframing the experiences that shaped our beliefs, we open ourselves to new ways of thinking and acting that better align with our goals.

Regression Toward the Mean

One of the key challenges in achieving goals is the natural tendency of life to regress toward the mean. This means we are not defined by our highest achievements or our lowest points, but rather by our average performance over time. Pain is a significant motivator that drives us to take action, but once we reach a comfortable state, we tend to slow down, causing us to regress to our average performance.

To overcome this tendency, it’s important to set goals that establish a minimum acceptable standard. By doing this, we ensure that even when we are at our peaks, we remain motivated to continue striving for more. Elevating our lows—raising the floor of our performance—is more critical than reaching new highs, as lower lows require more time and energy to recover from. By focusing on consistently raising our average, we ensure steady progress over time.

Conclusion

In the pursuit of any goal, the fundamental principle remains: energy, when applied over time through deliberate actions, shapes our reality. Achieving goals requires not only the proper application of energy but also the development of skills and the alignment of our belief systems with our objectives. Furthermore, we must be mindful of the natural regression toward the mean and ensure that our actions continuously elevate our lows, keeping us on the path of progress. By mastering these principles, we can achieve our goals more effectively, creating lasting and meaningful results.

Dissatisfaction and the Concept of Abstractions

Humans are naturally dissatisfied, and this dissatisfaction compels us to act using the means at our disposal to achieve our most valued ends. We strive to maximize our returns while minimizing our effort, constantly seeking to avoid pain and move toward pleasure. This tendency gives rise to what I call abstractions. In this context, an abstraction refers to the process of obtaining the same or greater value with less effort, where individuals or businesses trade control or customization for convenience, efficiency, or accessibility. The trade-off is asymmetrical—what is given up is outweighed by the benefits, resulting in a net positive outcome. I believe that the economy itself is an abstraction, where people obtain goods without having to produce them themselves.

Self-Sufficiency and Its Advantages

Historically, people produced what they needed to meet their basic needs, such as growing food and crafting tools. Self-sufficiency was once the norm. The primary benefit of self-sufficiency is complete control over what and how things are produced. Individuals could tailor their goods to their exact preferences, exercising full autonomy over the production process.

Limitations of Self-Sufficiency

However, self-sufficiency comes with significant limitations. Producing everything on one’s own is constrained by time, knowledge, and effort. Even when people knew how to make something, the process still required substantial labor. While producing the essentials for survival—such as cultivating crops, raising small animals, and constructing simple shelters—was feasible, more sophisticated desires emerged once these basic needs were met. These new desires demanded greater specialization, skill, and effort. Not everyone could dedicate the time to learn the necessary skills to build complex goods, and even if they did, the quality might be poor. Additionally, certain goods, like metal tools or vehicles, were nearly impossible for individuals to manufacture independently. As a result, people began to see the inefficiency of trying to produce everything themselves.

The Emergence of Abstractions and Trade

If humans didn’t have the natural inclination to maximize gains and minimize effort, they might have remained content with the limitations of self-sufficiency. However, driven by the need for efficiency and the desire for more sophisticated goods, people became willing to relinquish some control over the production process. This is where abstractions come into play: instead of doing everything themselves, individuals abstract away the complexity of production by relying on others who specialize in producing certain goods. They accepted this trade-off because the value they gained—saving time and accessing specialized goods—far outweighed what they gave up in control or customization.

Abstractions as the Foundation of the Economy and Market

This marked a significant turning point in human society—the emergence of trade. People were now willing to give up the autonomy of self-production in exchange for quicker access to goods, even if it meant accepting more standardized or generic products. Producing everything independently was simply impractical or impossible.

It was this very willingness to embrace abstractions that gave rise to both the economy and the market. The economy itself is an abstraction—a system that allows individuals to delegate the production of goods and services, relying on others to meet their needs more efficiently. The market became the platform where these exchanges and abstractions took place. Without the collective readiness to abstract away control in exchange for convenience, efficiency, and specialization, there would be no need for the intricate economic systems or marketplaces that shape human society today. Our willingness to seek these abstractions is the foundation upon which the modern economy and market are built.

Examples of Abstractions in Modern Life

Abstractions can be seen in various aspects of modern life. Take security companies, for example. Rather than handling home or business security themselves, people hire firms that specialize in protection. They give up control over managing security in exchange for convenience, expertise, and peace of mind. Similarly, investment funds provide another form of abstraction. Instead of managing their own investments, people entrust their money to professionals. By doing so, they relinquish control over individual investment decisions, trusting experts to manage their capital and reduce the complexity of navigating financial markets.

Even everyday products like smartphones offer a form of abstraction. A smartphone abstracts away the complexity of communication technology, allowing users to make calls, send messages, and browse the internet without needing to understand how the underlying systems work. Food delivery services eliminate the need for people to cook or shop for groceries, delivering ready-made meals with minimal effort. In essence, every product or service in the modern economy is based on some form of abstraction—each one simplifying life and making complex processes more accessible. Each abstraction delivers the same or greater value with less effort, ensuring that the trade-off benefits the user more than what they relinquish.

The Tendency Toward Greater Abstraction

One clear trend throughout history is the tendency toward greater abstraction. As new technologies emerge, they are often difficult to use and accessible only to a few. Over time, these technologies become easier and more widely adopted. Consider the evolution of computers: initially, they were large, complicated machines used only by experts. Decades later, they became personal computers accessible to the average consumer. Today, smartphones are powerful computers that fit in our pockets, requiring very little technical expertise to operate.

The same pattern can be observed with cars. Early vehicles were complex and required knowledge of mechanics and manual gear shifting. Over time, cars became easier to drive, with automatic transmissions removing the need to shift gears. The trend toward abstraction continues as we move toward self-driving cars, which will eventually eliminate the need for drivers altogether.

Thanks to this tendency toward abstraction, consumers experience easier, more convenient lives. Products and services that once required specialized knowledge are now accessible to almost everyone. At the same time, abstraction leads to greater worker productivity. As workers engage with tools and technologies that simplify tasks, they can accomplish more in less time, increasing efficiency. In turn, higher productivity leads to higher earnings for workers and contributes to overall economic growth. The economy grows because abstractions make both consumption and production more efficient, fueling innovation and expansion.

Employment as an Abstraction

With the rise of markets, the default economic role became that of the merchant. Merchants produced goods not just for their own use but to sell for money. However, being a merchant required a deep understanding of the market, including demand, competition, and pricing strategies. It was risky and demanded significant effort and management skills. Although merchants had the potential to become wealthy, they also faced the risk of failure and losing their invested capital. Moreover, success as a merchant took time, and returns on investment weren’t immediate.

Once again, people faced a choice. They could either take on the risks of becoming a merchant, with the potential rewards of wealth and autonomy, or trade those potential benefits for the security of working for someone else. By working for an employer, individuals could avoid the risks associated with entrepreneurship, such as investing capital and managing a business. Instead, they could focus on a specific role within the production process and earn a guaranteed salary, even though their labor often produced more value for the employer than they received in return. This trade-off is another form of abstraction—workers gave up control over the entire production process in exchange for the security of a stable wage and a lower risk of failure.

Employment thus became a solution for both employers and employees. Employers needed workers to increase productivity and grow their businesses, while employees benefited from the stability and convenience of a steady wage. By working for others, employees traded the potential for higher profits for the security of regular income, without having to bear the risks or complexities of running a business. This dynamic demonstrates how people consistently choose to engage in abstractions, giving up some freedom and control in exchange for greater convenience and safety.

Abstractions at the Core of the Modern Economy

In both trade and employment, individuals willingly sacrifice some autonomy for the sake of convenience and security. When trading, people give up the control they would have had by producing their own goods, in exchange for faster, more efficient access to what they need. Similarly, in employment, individuals trade the potential rewards of entrepreneurship for the safety and stability of a guaranteed wage. These abstractions—the willingness to relinquish control in exchange for convenience—lie at the core of the modern economy.

Ultimately, the entire economy emerged because people were willing to exchange their freedom for the convenience, efficiency, and safety provided by indirect trade, specialization, and employment. This principle of abstraction continues to define modern economic structures today and drives the growth of the economy by making life easier for consumers and workers more productive.

Introduction

It’s a given that success relies on intelligence, but we seldom stop to consider what intelligence truly entails. Is it merely the ability to solve mathematical problems, or does it encompass something deeper—an understanding of reality itself? In this essay, I will explore the concept of “Meta-Intelligence,” a term I have coined to describe the most fundamental aspect of intelligence, which centers on understanding objective reality. I will delve into how Meta-Intelligence forms the foundation for sound decision-making and how cultivating this ability can lead to success in any field.

Defining Intelligence

Before delving into Meta-Intelligence, we must first establish what intelligence is. Traditionally, intelligence is seen as a general cognitive ability that includes reasoning, problem-solving, learning, and adapting to new situations. While many definitions exist, and I believe it’s impossible to create a single definition that encompasses every aspect, they all share one common thread: making the right decisions. In any context, intelligence involves the capacity to act effectively, which requires making sound decisions.

Making the right decisions depends on understanding objective reality, which refers to the state of things as they truly exist, independent of personal biases or beliefs. The laws of physics and the principles of logic, for example, are constant and unaffected by individual opinions. They represent the unchanging truths of the physical and logical world. Recognizing and aligning our decisions with these truths is essential for success.

This is where what I call “Meta-Intelligence” comes into play. Meta-Intelligence is the fundamental ability to grasp the true nature of reality, serving as the foundation for all other forms of intelligence. By understanding objective reality at its core, we can navigate any domain with greater success.

The Role of Beliefs

Meta-Intelligence emphasizes the importance of beliefs, which shape how we perceive and interact with reality. A belief is any piece of information we accept as true, filtering how we interpret new data and make decisions. Every conclusion we reach is based on a belief, making it crucial that our beliefs align with objective reality to guide sound decision-making.

We form beliefs through logic, experience, and opinion of others. We tend to trust ideas that align with basic logical principles, namely the law of identity, the law of non-contradiction, and the law of excluded middle. Additionally, we rely on our sensory experiences, common sense, authority figures, and traditions.

However, these methods of forming beliefs can be flawed. Our experiences and the opinions of others can mislead us. We might mistake exceptions for rules, create false causalities, or accept something as true simply because it is widely believed, endorsed by an authority, or rooted in long-standing tradition. Even beliefs that seem logical might be unreliable when distorted by cognitive biases or fallacies.

Thus, it’s vital to critically evaluate every piece of information before accepting it as a belief, ensuring it aligns with objective reality. Beliefs not rooted in truth create a distorted worldview, leading to poor decisions. Over time, these beliefs can become deeply ingrained, making them harder to challenge and reinforcing misconceptions about reality.

Evaluating Propositions

To further explore how beliefs shape our understanding of reality, it’s important to delve into the concept of propositions. A proposition is a statement that can be either true or false. Every piece of information we encounter is essentially a proposition, and as discussed in the last section, a belief is a piece of information we’ve accepted as true and use to interpret and filter new information. Therefore, beliefs are also propositions. Evaluating these propositions is key to ensuring our beliefs align with objective reality.

We should treat every proposition we encounter as false until proven otherwise. To properly examine a proposition, we must first recognize that behind every proposition lies an argument. The proposition serves as the conclusion of this argument, supported by premises—statements that logically lead to the conclusion. For the argument to be sound, its premises must be true, and the conclusion must logically follow from them.

Consider the proposition: “A rose needs sunlight to grow.” This is supported by two premises:

• All flowers need sunlight to grow.
• A rose is a flower.

This is an example of a sound argument, meaning that the conclusion logically follows from the premises (validity), and the premises are also true in reality. Since all flowers, including roses, need sunlight to grow, the argument is both valid and sound. A sound argument provides a strong foundation for the truth of its conclusion.

Now take the proposition: “A salmon can fly.” It’s based on this argument:

• All fish can fly (false).
• A salmon is a fish (true).

Although this argument is valid, meaning the conclusion logically follows from the premises, it is unsound because the first premise is false.

This distinction between validity and soundness is key. A valid argument ensures logical consistency, but only a sound argument leads to true conclusions.

Understanding Systems

Many propositions exist within larger frameworks called systems. In systems, propositions are often not immediately obvious, requiring a deeper understanding of the subject matter to uncover them.

A system consists of inputs, processes, outputs, and feedback. The system has an expected output (what we want to achieve) and an actual output (what we actually get). The expected output serves as a benchmark to measure the success of the system, while the actual output reflects the real result. The gap between these two outputs provides feedback, which is crucial for assessing and improving the system’s performance.

The inputs of a system must meet a defined quality standard. For example, if you’re running a marketing campaign on Facebook Ads, your inputs could include things like your budget, the creative content, and the target audience. Each of these inputs must meet a certain quality (e.g., sufficient budget, an ad that sparks the prospect’s interest, and a well-defined audience) to ensure the system operates effectively.

The process itself consists of several interconnected steps, and each of these steps also has its own expected output and actual output. These smaller expected outputs are what drive the system toward the overall expected output. For instance, in a user acquisition system, one process step might involve showing the ad to the right audience, where the expected output is a 3% click-through rate (CTR).

For example, consider a marketing campaign using Facebook Ads. The inputs include:

• Ad Budget: The amount of money set aside for the campaign.
• Creative: The visual or text-based content used in the ad, which needs to capture the target audience’s attention.
• Audience Segmentation: Selecting the right audience to ensure the ad reaches the people most likely to engage with it.
• Landing Page: The webpage users land on after clicking the ad, which must persuade them to sign up.

Each of these inputs must meet a certain quality (e.g., a sufficient budget, compelling creative, the correct audience, and a persuasive landing page) to ensure the system operates effectively.

And the process steps might include:

1. Ad Impressions: The ad is shown to the targeted audience.
2. Ad Clicks: Users click the ad (measured by the Click-Through Rate, or CTR).
3. Landing Page Visits: Users land on the webpage after clicking the ad.
4. User Sign-Up: Users sign up on the landing page (measured by the conversion rate).

Let’s assume the expected output for this system is to acquire 100 sign-ups per day with a $10 CPA (cost per acquisition). However, if the actual CPA is $14.20, this difference (the feedback) shows that the system is underperforming. This feedback prompts us to investigate the system’s components, such as whether the CTR is lower than expected, the conversion rate on the landing page is underperforming, or the targeting isn’t reaching the right audience. By identifying where the system is falling short, we can adjust these components to optimize the system and achieve the desired CPA.

Analyzing System Feedback

Improving a system involves breaking it down into its core components and relationships. Start by clearly defining the system’s components—inputs, the process, and the outputs (including both expected and actual outputs, if the system is already operational). Next, identify the relationships between these components to see how they interact and influence one another. Additionally, it is important to consider how external environmental factors impact the system and its components.

If you’re trying to improve the actual output, the feedback—the difference between the actual output and the expected output—becomes your proposition, while the current state of the process components, inputs, and their relationships act as the premises supporting this proposition. If the system isn’t operational yet, the expected output itself serves as the proposition.

For instance, let’s take the Facebook Ads system example again. If the system is underperforming—let’s say instead of acquiring 100 sign-ups daily at a $10 CPA, the system is only acquiring 70 sign-ups at a CPA of $14.20—this feedback becomes the starting point for analysis. In this case, the feedback (higher-than-expected CPA) becomes your proposition: “The system is acquiring customers at $14.20 per sign-up instead of $10.” The current state of the inputs (e.g., ad budget, creative, audience segmentation, landing page design), process (e.g., ad impressions, clicks, landing page visits, and sign-ups), and actual outputs (e.g., CTR, conversion rate) serve as the premises supporting this proposition.

At this stage, you can formulate a specific argument to investigate where the issue lies. For example, one possible proposition could be: “The higher CPA is caused by underperforming creative.” The premises supporting this argument could be:

• Premise 1: The current Click-Through Rate (CTR) is 2.11%, which is below the expected 3%.
• Premise 2: Low CTR is usually indicative of weak or unengaging creative.

Given that both premises hold, the conclusion logically follows: “The creative is causing the higher CPA.”

However, instead of stopping at this conclusion, you can further question the first premise: “The CTR is 2.11% because the ad creative is not performing well.” This premise is itself a proposition, and its truth depends on additional premises. To explore this further, you can break it down:

• Possible Premise 1: The ad creative is not capturing attention, leading to fewer clicks.
• Possible Premise 2: The audience targeting may be incorrect, leading to lower engagement.

Upon reviewing the targeting, you confirm that the audience segmentation is accurate, leaving the ad creative as the most likely culprit. This analysis leads to the conclusion that the low CTR is indeed caused by a poorly designed ad that fails to engage the audience effectively.

With this understanding, the next step is to revise the ad creative—making it more engaging, attention-grabbing, and aligned with the audience’s interests. By addressing this key issue, you can expect an increase in CTR, which will, in turn, reduce the CPA back to the target level of $10.

Additionally, external factors may impact the system’s performance. For example, if you’re running a fitness app, you may notice higher sign-ups in January due to New Year’s resolutions but lower engagement in other months. Though these external factors lie beyond the system’s control, they still influence performance.

If the system is already operational, you can establish a timeline to observe how these relationships and the environment have changed over time and how the system has evolved to its current state. Remember, the expected output is the benchmark for assessing how well the system is functioning. If the outcome met expectations in the past but no longer does, you can identify when it stopped performing effectively and determine what changes occurred in the process, the quality of the inputs, or any external factors that may have affected the outcome.

Breaking Down to First Principles

Finally, in examining systems and their components, we might encounter situations that seem resistant to change, seemingly inherent to a component, relationship, or even the entire system. However, you should never accept this as true simply based on intuition or because someone else proposes it. This belief is itself a proposition and must be scrutinized like any other.

To properly assess whether a situation is truly immutable, you must break down its premises layer by layer until you reach the point where further breakdown is impossible—this is known as the first principle, the most fundamental premise. A first principle is the essence of something—the most basic truth that cannot be reduced any further. By identifying and understanding these first principles, you can determine whether a situation is inherently unchangeable or if it can be modified. If the situation stems directly from a first principle, it is necessary and, therefore, not subject to change. However, if it arises from a premise built upon the first principle, you can explore the reasons behind its emergence and identify ways to address or improve the system accordingly.

For example, imagine a company running Facebook Ads believes that a click-through rate (CTR) above 4% is always bad because, although it attracts more clicks, these visitors are often unqualified prospects driven by curiosity rather than genuine interest. This proposition (“A CTR above 4% is bad”) must be examined by breaking down its premises. If the company seeks to reduce the problem to its first principles, it would begin by analyzing what CTR represents at its core. CTR is simply a reflection of users clicking on an ad—clicks occur when the ad captures their attention and piques their interest. This basic principle doesn’t inherently suggest that a high CTR is problematic; rather, it only shows that the ad is engaging.

Upon reaching this understanding, the company can explore ways to increase CTR while maintaining qualified prospects. For instance, targeting an audience with a higher level of awareness—those more ready to buy—could lead to higher CTRs while still attracting qualified leads. Additionally, even if curiosity-driven users click on the ad, the landing page copy can be optimized to convert this low-consciousness audience, thereby improving conversion rates and overall profitability. The initial belief about high CTRs being detrimental is revealed to be a misconception, and adjustments can be made to leverage higher CTRs more effectively.

In contrast, some situations are inherently immutable. For example, if an advertiser attempts to generate a high CTR without showing content that interests the prospect, it simply won’t work. Breaking this down to the first principle of CTR reveals that for a user to click on an ad, it must capture their attention and interest them enough to take action. This principle is inherent to the nature of user engagement and cannot be bypassed. Therefore, trying to achieve a high CTR without addressing these elements is futile, as it contradicts the core nature of how CTR is generated.

Conclusion

In the end, intelligence is about grasping the objective reality that underpins everything we encounter and applying it to our decision-making. This demands a disciplined approach—identifying the propositions within any situation, breaking down their premises, and analyzing them logically. By doing so, we align our decisions with the truth, ensuring they are not only effective but also rooted in a deep understanding of reality. This process of critical thinking and logical analysis is what ultimately guides us toward success in any field.