In life, start with the idea that nobody owes you anything. Give first, and receive later. Some people are malicious, but most people are not, they won’t forget when you give. Don’t expect to receive, before handing in an idea, a dollar, a line of code, a new contact. Don’t be stingy.
Search for “low agency people vs high agency people” on the Web.
Some person doesn’t want to work with you? It’s a form of feedback from reality. Ask yourself why, and blame the person later, when you are absolutely sure the person is wrong. More often than not, either you are at fault, or your expectations or simply not in line with the person’s expectations.
There are individuals and there are systems of people. Learn to forgive individuals. Most individuals are constrained by systems. Become strong enough to fight a system rather than a single person. This is generally true, but even more important when you have higher ambitions.
Your first time won’t be glorious. But success comes only to those who tried a first time, a second time, a third, etc., until success.
Ideas are almost worthless, they come and go. Everyone has an Idea. Billion ideas are born everyday, and as many die the same day. Execution is priceless, because it’s what makes Ideas persist in time. Execution is rare, because time is the most precious thing a person has. Stop being scared of people stealing your Ideas. As Sam Altman said once, even if you dropped a 50 pages document explaining your company’s operations in detail on Google’s COO desktop he wouldn’t read it. People are too busy minding their own business.
One day, you will scream your product’s name on top of the Empire State Building to make it famous. Why are you scared sharing ideas with your friend?
If you think that your idea can be replicated easily on the sole base of it leaking, it means that you are overestimating your idea. The long term sustainability of a product is generally fueled by, at least, one of the following elements (of course, the list is not exhaustive):
network effects
economies of scale
lock-in
an underlying relatively expensive infrastructure
strong legacy branding
it is domain specific and you have data that very few people have
Personal advise: don’t try to hide an idea, because if you do that, it will send a negative signal to an experienced person, as hiding an idea attests of a lack of experience in the founding space.
Academia and intelligence are two different things.
But, don’t overestimate intelligence. With time, action and persistence beat an intelligent but inert entity…by far.
Scarcity is the mother of innovation. You don’t need much to move.
Don’t get too comfortable. Stagnant waters rarely turn out well.
Don’t hope that contracts will protect you or help your business thrive. This is not how you grow with people.
Contracts are, more often than not, rent seeking tools. Innovation precedes contracts, not the other way around.
Understand that a product is neither you alone, or the other. It’s that third person which is your relation with others.
Watch the big picture. Probabilities and Compound Interest are your best friends.
Anyone can maybe provide a good advise, but most people will provide a bad advise. Anyone has an opinion on everything, but very few are experts in your specific domain. The probability of getting a good advise increases dramatically if you follow advises of people who actually succeeded before you, in your specific domain.
If you need someone to motivate you to start a business, it means that you are not ready to start a business. Don’t wait for motivation. Just do it.
Be tougher on yourself than on other people. It will liberate you, as it’s way easier to change yourself than others.
Don’t apply the employee-employer contractual relation in business. People are matrix-ed by this. People are also matrix-ed by intellectual property in Western countries. If you want to break the matrix, learn to gain trust. Learn to give. The quality of your life will dramatically increase and you’ll understand that personal relations are the building block of everything. Money will flow later.
You want to stand out while reaching out to someone who you really care about? Take a pen, write a letter, wait one day, read it again, correct it. Go to the post office, send it.
Health is the real treasure. Other than that, nothing bad can ever happen. There is no such a thing as a bad event. This doesn’t exist. You succeeded once, twice, you have the tools to succeed a third time.
If someone you love is stuck, instead of simply judging or leaving them, be the human you once wished to meet. That is, a human who finds a way to build at least a minimal infrastructure around that person, which can serve as a seed for success. Not only does this help the person, which is an altruistic victory in itself, but it also trains you to be clear, efficient, and capable of designing systems for people who have not reached your level of competence or stability. This is one of the core principles of product design.
Never ever claim victory. Because the world is dynamic. Therefore, never settle down.
You feel mediocre? It’s nobody’s fault. It’s not your mother’s fault, it’s not the president’s fault, it’s not your dad’s fault, it’s not your friends fault, it’s not your partner’s fault, it’s not your teacher’s fault. You are not a kid anymore. You think I’m wrong? Stop finding excuses. Change your condition. You feel that the system is unfair? Propose better solutions and Fight it.
Almost everytime time somebody wants to release a piece of tech or wants to do so, you’ll find someone saying “it already exists”. But distribution , rather than pure innovation, is more often than not the key point, and timing also. This is also true for the seemingly most “advanced” technologies. This was even Meta’s […]
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Almost everytime time somebody wants to release a piece of tech or wants to do so, you’ll find someone saying “it already exists”.
But distribution , rather than pure innovation, is more often than not the key point, and timing also.
One needs to understand that the real deal is incremental change + distribution + the inertia of big players that lack the agility in terms of product release. Moreover, markets are very different across geographies, sectors, client types, etc…. Seems obvious, but apparently not that much.
I still hear “it already exists”, almost every every week.
There is very rarely a winner takes-it-all logic where network effects are not the driving concurrence force.
Also note that the first mover advantage is rarely enough to monopolize a market. DeepSeek showed it.
Google was far from being the first search engine. See this article.
Non classébusinesscognitivecorporateentrepreneurshipinformationinnovationleadershipmiddle classsocietytechtechnology
The first title I chose for this article was ‘Thinking About the EU’s Technocratic Tendencies and Talent in the Information Age.’ However, I felt like it sounded too academical and wasn’t powerful enough to emphasize the urgent situation Europe is facing. During my research I stumbled upon an article of the World Economic Forum (WEF) titled Is the […]
The first title I chose for this article was ‘Thinking About the EU’s Technocratic Tendencies and Talent in the Information Age.’ However, I felt like it sounded too academical and wasn’t powerful enough to emphasize the urgent situation Europe is facing.
My idea is the following: What if we reverse the problem and build what we could call a “technological middle class” that revolves around what is conventionally considered a threat (technology)?
India has attempted to create something similar. So, my idea is far from revolutionary, but it is worth contemplating. This leads me to a rather cynical question: what have we Europeans chosen to specialize in over the past 40 years?
Accenture, a technology consulting firm, where I used to work, had 500,000 employees worldwide, with 250,000 based in India alone. Today, in 2024, it’s 300,000, in India alone. This speaks volumes. China has been the manufacturing hub for physical products, and it seems that India has taken on the role of the world’s hub for non-physical product manufacturing.
Deindustrialization in Europe has drastically reduced traditional manufacturing jobs and the numerous sectors that relied on them, with much of this work relocating to the East in the context of globalization.
This transformation has led to a shrinking middle class, which has become increasingly reliant on the expansion of media and information companies, often referred to as the ‘Information Economy.’
Building on this observation, materially, where are we, Europeans? Us, the middle class, where do we want to go?
There is a deep disconnection between the way we consider technology and the way technology considers us. People tend to consider technology as an elitist artefact, while none of us is sparred from Information Technology.
I will share some of my personal corporate story, as I believe it reflects what is at stake on a larger scale within the European Union.
Skilled on Paper
Why Europe’s Corporate Confidence Misses the Mark on Tech Progress
I’m only 30, but I’ve worked with many companies, both startups and multinationals (Accenture, Deloitte, and other large Insurance and Banking institutions that I can’t disclose), largely due to my experience in consulting.
I’ve noticed a recurring pattern regarding “social capital” and companies’ self-assessment:
Many board members (including heads of HR) view their talent pool as highly skilled and often even “exceptional”, frequently taking diplomas as proofs. However, they rarely take into consideration the significant and obvious diploma inflation of recent decades. In the 2022/2023 school year, French universities registered 2.8 million students, an 18% increase from ten years ago.
The % increase that spans over decades is way higher. The number of students is higher, but the rate of technological progress in Europe is declining.
The same attitude applies to the tech stacks promoted by the boards and heads of HR, which is paradoxical given their often limited technical knowledge to assess them accurately (it’s not a critique…as it’s not their core domain often) .
European’s Technocratic Insularity Blocks Business Innovation
The technocratic-structure (you can think of it being a piece of the broader concept of “bureaucracy”) is fundamentally tied to the ever increasing division of labour. As roles become highly specialized, companies and institutions tend to recruit individuals with specific credentials or technical skills suited to those narrow roles. This has a strong consequence: an ever increasing homogeneity of profiles in corporations and institutions. And this, obviously, hinders innovation.
Interestingly, the most outstanding individuals within this “social capital” don’t necessarily have the most prestigious diplomas.
I apologize for using a cliché reference here: but Steve Jobs, a humanities dropout, would have strongly agreed on this. Read his biography, written by the amazing Walter Isaacson.
Airbnb’s founder, Bryan Chesky, was a designer.
Meta’s Marck Zuckerberg lacks of a formal degree, even if he attended computer science classes, along his psychology classes.
Melanie Perkins, the CEO of Canva, a company valued at $39 billion, majored in communications, psychology, and commerce.
Reid Hoffman, co-founder of LinkedIn and Inflection AI, holds a master’s degree in philosophy.
Sergey Nazarov, the CEO of Chainlink, a decentralized oracle network that has processed over $17.3 trillion in total transaction value, earned a bachelor’s degree in philosophy and management from New York University.
Many of their career paths would have been nearly impossible in Europe due to early social disqualifications, often reinforced by self-sabotage and limited belief in non-traditional paths.
These are not exceptions. The U.S. tech industry demonstrates indeed a more inclusive approach to hiring, valuing skills and experience over formal education. Companies like Google and Apple have removed degree requirements for many positions, focusing instead on practical skills and competencies. There is a deep difference from what I witnessed as job seeker in Paris. Studies confirmed my experience: France’s prestigious Grandes Écoles, such as École Polytechnique and HEC Paris, have a significant influence on the tech industry. A study by digital sociologist Jen Schradie found that:
93% of France’s 40 most promising startups had at least one founder that graduated from a Grande École
This percentage is mind-blowing.
In contrast, in the UK (which is no longer part of the European Union), a 2019 report by the Sutton Trust found that only 12% of tech CEOs had attended Oxford or Cambridge, suggesting a broader range of educational backgrounds among tech leaders.
The following statement I am going to make is thought-provoking, but given the stark results, it might serve as a necessary wake-up call: from personal experience, there seems to be an inverse correlation between a person’s academic excellence and their ability to generate business ideas. And don’t get me wrong, I am not making any assumptions about intelligence. This behavior has a perfectly logical rationale.
In a structuralist fashion, I am pointing the written prerogatives and the legal norms (that constitute the European technocratic-structure). Those end up forming individual habits. The structure transcends the individual. The European technocratic-structure is being criticized here, not the individual per se.
The technocratic structure absorbs talents into predefined and narrow corporate roles, hindering their creative potential. Here is the rationale: the better the diploma, the more attractive the student becomes to the technocratic structure.
Evidence shows that there is a strong correlation between elite educational backgrounds and recruitment into EU technocratic positions.
If you want to predict the future of a socioeconomic entity, whether it be a corporation or Nation, then watch where the incentives are.
Since alternatives to the institutional or corporate job (in terms of incentives strength, see the “Europe’s Risk Aversion Hinders Growth” section below), we have a strong, self-reinforcing feedback loop.
There is a significant gap between the incentives offered by venture capital-backed opportunities in Europe and those proposed by the European ‘technocratic structure,’ which encompasses large traditional corporations and European institutions. Naturally, the latter holds greater regulatory and decision-making authority.
These figures represent the basic monthly salaries and do not include additional allowances such as expatriation, household, or dependent child allowances, which can significantly increase the total remuneration. For instance, expatriation allowances are typically 16% of the basic salary. No comparison is possible with the average salary of one of Europe’s economically leading Nation: France. As of November 1, 2024, the French minimum wage (SMIC) is set to a monthly gross salary of €1,801.80 for a standard 35-hour workweek.
Of course, the problem is not people being paid well. The problem is that these employees are incentivised and retained to continually reinforce Europe’s regulatory technocratic structure.
The well-known tech expert Andrew McAfee of MIT in his article titled European Competitiveness, and How Not to Fix It points out Europe’s heavy regulatory tendency, and notably the so-called “General Data Protection Regulation” (GDPR).
GDPR one of the biggest European technological regulatory byproduct, is a creation of this technocratic structure. It significantly restricts data access for innovative startups, depriving them of the critical resource needed to drive technological advancement: data, which is often referred to as the ‘oil’ of the tech industry. This creates a barrier to innovation, as only large corporations (which are predominantly non-European) have the resources to navigate compliance, enabling them to access and leverage data at scale. This consolidates their market dominance and stifles competition.
To put it briefly: Europe has shot itself in the foot—one that was already injured.
While Europe’s technological regulations are harmonized and uniformly enforced across its member states, its capital markets remain deeply fragmented. This disparity creates a paradox:
Europe succeeds at regulatory standardization, but not at unifying capital, the fuel of venture
Equity financing as a percentage of GDP in Europe is half that of the US. Without an integrated capital market, there is no sufficiently large pool of capital to fund transformative technological initiatives. This mirrors the 19th-century American railroad boom, where fragmented local markets initially stifled long-term projects. Only when the U.S. unified its financial markets could it attract domestic and foreign capital to build the large-scale infrastructure essential for growth.
The Technological Entrepreneurial Class: Europe’s Missing Counterbalance
The European Innovation Scoreboard 2024 indicates that Europe lags behind in fostering entrepreneurial talent and innovation compared to other regions.
I can already anticipate the counter-argument: “The U.S. also has a very strong technocratic elitist caste.” That may be true. But, the U.S. possesses a fundamental counterbalancing structure that we lack entirely: an entrepreneurial class fueled with capital (see the “Europe’s Risk Aversion Hinders Growth” section below for evidence) that actively competes with the traditional financial, and more broadly, corporate sector. This dynamic reflects the classic historical contrast between the East Coast and the West Coast represented by the Silicon Valley. A caricatural view is to consider the split as new-money vs old-money. This competition between these two structures creates a kind of national emulation.
This brings me to a theory I’ve developed about France (I was born in Paris, studied and worked in Paris), and more broadly, Europe (I currently live in Luxembourg, worked there, I have an Italian Passport and I am half Serbian).
There is a notable lack of what might be called a “technological middle class“. The European Mind is very academically oriented, which is detrimental in an Information Economy.
Lessons from Soviet Planning
This lack of competition between structures might also be the reason why such an European bureaucratic and technocratic structure developed. The non-elected deciders (this fact has it’s weight, because they are neither selected by the people, neither validated by an external market) are academically oriented and are paid to publish. Publishing is at the core of academia. And, as the people who work for the European Union needs to justify themselves, they publish a lot. It is time consuming, hence expensive. Being peer-selected is not a problem in itself, in a scientific setup. But the problem arises when we start to consider innovation and policies that affect hundred of millions of citizens, based almost exclusively on narrow technical details.
Michael Ellman (who was a foreign member of the Russian Academy of Economic Sciences and Entrepreneurship), in his book titled “The Rise and Fall of Economic Planning in the Soviet Union“, examined the Soviet Union’s planning system and its technocratic foundations. Another interesting book, published by the MIT, is Benjamin Peters’s “How Not to Network a Nation: The Uneasy History of the Soviet Internet“. He examined Soviet attempts at developing a cybernetic economic network, which were heavily influenced by scientific and technocratic ideologies. The book highlights how the lack of flexibility in centralized, scientific planning prevented innovation and adaptability.
Technocracy is what the unique Soviet Party tried to implement in the failing USSR, and what, unfortunately, Europe seems to tend towards, while simultaneously criticizing the re-rise of the “Communist” East.
Europe’s Risk Aversion Hinders Growth
What is that distinguishes the thousands of years of history from what we think of as modern times? The answer goes way beyond the progress of science, technology, capitalism and democracy. […] The revolutionary idea that defines the boundary between modern times and the past if the mastery of risk: the notion that the future is more than a whim of the gods and that men and women are not passive before nature.
Against The Gods, Peter L. Bernstein
A wealthy and thriving society stands on, at least, these three pillars: Talent attractiveness, Information circulation levels and what we could call a “Risk culture”. I recommend Bernstein’s Against the Gods about this topic.
Talent attractiveness relies in majority to capital, and capital relies to a relatively high risk appetite and capital availability: three pillars that the United States got right.
Just as one single example, every batch (there are 2 batches per year), thousands of founders and aspiring founders from all around the world apply to Y Combinator (whose companies have a combined valuation of over $600 billion). The ‘Standard Deal’ offers $500,000. This figure alone would make headlines in many European local newspapers. In the US, it’s just ‘standard’.
You will find below a synthesis of YC’s request for startups (2024).
“Markets look a lot less efficient from the banks of the Hudson than from the banks of the Charles.” Fischer Black The article is in constant evolution. It’s more about racing thoughts than a formally established demonstration. A posteriori vs a priori: grasping a fundamental epistemological framework I believe that there is a misconception in […]
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“Markets look a lot less efficient from the banks of the Hudson than from the banks of the Charles.”
The article is in constant evolution. It’s more about racing thoughts than a formally established demonstration.
A posteriori vs a priori: grasping a fundamental epistemological framework
I believe that there is a misconception in assuming that markets are inherently “efficient”, implying they flawlessly incorporate all available information via prices. A market’s nature isn’t fixed as “efficient” or “inefficient.”
A more balanced standpoint is necessary for analysts to adopt.
While markets might lean towards efficiency through arbitrage, the simultaneous existence of arbitrage opportunities and market gaps contradicts the assumption of complete efficiency.This paradox challenges the seemingly evidence of efficiency. When we say it’s “efficient”, we assume totality; or no space for error, like when we remind ourselves the gravity equations. But Economy is a social science, not a physical one.
The arbitrage and non-equality (disparity) of the agents argument
The belief that markets “tend” towards equilibrium due to arbitrage suggests that sustained equilibrium isn’t their constant state.The persistence of arbitrage opportunities highlights that the dissemination of information isn’t as efficient as presumed by the efficient market hypothesis.
Markets are not organisms per se, they are aggregations of human actions (let’s call a “human” an “agent” to sound scientific). And because agents themselves are not efficient, markets can’t be absolutely efficient, as simple as that. Some humans are more quick witted than others or technologically superior. “Efficiency” would assume equality of agents, like communism does. But some agents « are more equal than others » (this is a reference to the Animal Farm).
The Unpredictable Nature of Markets argument
As G. Soros reminds us, markets are in constant disequilibrium. See Soro’s Alchemy of Finance and reflexivity theory, he delves into these questions.
Not only is the efficiency theory precarious, but the equilibrium even more. This doesn’t mean that these concepts are entirely incorrect, but rather that they exist in a precarious state.
What comes first, or a priori? The fact that oil prices “equilibrate“ (eventually) after a pipeline was bombarded in the North Sea, or the fact that a priori, a political decision was taken to bombard a pipeline?
Both efficiency and equilibrium theories are tenuous, not entirely incorrect but inherently unstable. In the case of oil price equilibration after an attack on a pipeline, equilibrium remains precarious due to the influence of psychological expectations related to warfare. There is no regression to any mean, there is a regression to human expectations, which can be way more volatile than anything that preceded. Historical data is not helpful in such events. Mathematical models struggle to encapsulate the intricate confluence of expectations that can be very different and even opposed across the globe, whereas prices remain globally integrated. A polarized world contradicts Fukuyama’s “end of history” notion. I recommend Samuel P. Huntington regarding this topic.
The Psycho-Sociological Fabric of Efficiency Dominance
I think that the prevalence of the efficiency theory is an heritage of a uni-polar World where Wall Street was king and where seeing physically an equilibrium created in the middle of an Exchange has impacted the way economists built their mathematical models. Things are changing, and so must our framework(s) of thinking.
If you take the case of currencies, there is a balance of pairs, because there is balance in power. Price evolution reflects power, and not supply and demand of traders. A priori comes National Power, not the trader. The average trader tries to surf the liquidity flux wave, a posteriori, he’s not fundamentally affecting the ocean’s fluid dynamics.
While the latter could happen in extreme scenarios, it’s not the rule.
Consequently, successful speculators prioritize a holistic understanding of long-term price dynamics. The a priori/a posteriori framework is pivotal for pragmatic investors seeking success.
Recognizing this epistemological reality can lead us toward a more accurate understanding of market dynamics.
The interesting aspect of open source is that the value it provides lies not in the software itself, but in the understanding of it and the ability to monetize its maintenance and it’s ecosystem. Open Source and Monetization are not antagonist. From a moral or political perspective, this can be considered somewhat superior. What we […]
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The interesting aspect of open source is that the value it provides lies not in the software itself, but in the understanding of it and the ability to monetize its maintenance and it’s ecosystem. Open Source and Monetization are not antagonist.
From a moral or political perspective, this can be considered somewhat superior. What we appreciate in this context is not the possession of some software or a piece of hardware, but the accumulation, dissemination and management of knowledge, whether it be on an individual or organizational level.
Linux is a great example. It is Open Source, but this didn’t prevent 90%+ of the top one million web servers globally to use it. A thriving and lucrative ecosystem was built upon it (security, maintenance, courses,…).
It is interesting because it seems to resolve some capitalistic problems that seem like zero-sum games.
Of course, not everything can or should be made open source, but it’s worth contemplating the implications of an Open Source mindset.
If you’re interested by how and why to use Open Source strategically, you should check up Matt Rickard’s post about his Taxonomy of Open-Source Strategies.
Intro We know well that this machine does not think. It is us who made it, and it thinks what we tell it to think. But if the machine does not think, it is clear that we ourselves do not think either at the moment when we perform an operation. We follow exactly the same […]
We know well that this machine does not think. It is us who made it, and it thinks what we tell it to think. But if the machine does not think, it is clear that we ourselves do not think either at the moment when we perform an operation. We follow exactly the same mechanisms as the machine.
Some claim that this quote is from Jacques Lacan, but I couldn’t verify it.
Targeted public
This article is niche, and a little bit unconventional, I must admit. It’s a wander, rather than an attempt to get to any conclusion.
This wander, in my humble opinion, makes it interesting, as it attempts to bridge several domains, namely philosophy and programming.
The goal is to foster interest and provide hints to an interdisciplinary researcher.
Having spent years at Sorbonne University studying philosophy (among other things), I find myself inclined to synthesize and define Philosophy as an endeavor to unveil timeless universalconcepts and truths. By this, I mean conceptual frameworks that were valid 5000 years ago, remain true today, and will likely hold true in the next 5000 years. Whether someone has successfully achieved such a thing or not remains a subject of debate. Other fields also grapple with similar questions, albeit employing different tools than those found in the philosopher’s toolkit.
What is programming? (And what could a philosophy of programming be)
At root, programming is about creating abstractions in order to solve problems, thanks to data structures to handle data storage, and the whole under the constraint (and thanks to the help) of logic and discrete mathematics. The concept of discreteness is important for the coming lines. Keep it in mind.
I gave a common definition to programming, and many programmers would say that this is obvious at first, and even maybe stupid to re-state. But bear with me.
Towards higher (and higher) degrees of abstraction
At a certain point of computer science history, programming was very physical, one could literally feel the “physicality”of it through punched cards. “Low-level” languages such as Fortran, COBOL, Assembly or ALGOL were used along with these.
A single program deck, with individual subroutines marked. The markings show the effects of editing, as cards are replaced or reordered.(source: Wikipedia)
Then, some notable inventions were made like magnetic tapes, magnetic disks, floppy disks, CDs, etc…
These new storage methods were accompanied by the emergence of “medium-level” programming languages, like C (even-though C is considered today as a low level language), then “high level” languages (like Perl or Python).
High-level languages can express more in fewer lines (because they are built upon lower level languages). To put it in another way, those higher level languages are more “explicit” and more human readable (this point is fundamental).
Now, returning to the initial question of “What is programming,” some individuals from the 1950s might argue that Python is “less” of a programming language compared to using ALGOL for example, which was “closer” to the machine and binary logic. However, a Python developer today would certainly not consider himself any less of a “programmer” than those from the 1950s.
The question now is: Until which level of abstraction will it still make sense to say that we “program”? Does it matter? (this is not a rhetorical question)
In my view, programming (to be distinguished from computer science) can be positioned at the intersection of philosophy, science and engineering. It possesses serious logical constraints (as evident in discrete mathematics), but it cannot be considered a science in itself, if we define science as a research process. There is a fundamental pragmatic dimension to programming. We program to act upon something immediately, and it’s more of an Art (we talk about “software craftsmanship” by the way), as it has less constraints than engineering. But, programming shouldn’t be seen solely as a function to reach a goal either; rather, it displays more generally what is computationally possible.
Programming as a “Reality Discretization tool”
I think that a good way to approach the question of programming from a philosophical point of view is to view it is a Reality Discretization tool. (I will develop this point as I continue this article)
Why? Because whatever level of abstraction you use, it seems like you will always need to deal with data structures that are inherently discrete, even if the function you apply on an element is very abstract (i. e is characterized by a high level of encapsulation). In this sense, programming deals with segmenting reality we could say. Think of computer graphics: virtual reality is made of a finite number of vertices (points that define the shape of objects), even when those are “infinitely generated” (think of a universe like Minecraft to use a trivial example). The number of vertices that can be rendered in a game is typically limited by factors such as computational power, memory, and rendering capabilities of the hardware and software involved.
What is philosophy?
For Deleuze and Guattari in What is Philosophy?, then, science deals with properties of constituted things, while philosophy deals with the constitution of events. Roughly speaking, philosophy explores the plane of immanence composed of constellations of constitutive forces that can be abstracted from bodies and states of affairs. It thus maps the range of connections a thing is capable of, its “becomings” or “affects.” Science, on the other hand, explores the concretization of these forces into bodies and states of affairs, tracking the behavior of things in relation to already constituted things in a certain delimited region of space and time (the “plane of reference”). How do concepts relate to functions?
This snippet is in french, but what it tells us essentially is that philosophy consists in creating concepts. He immediately dismisses the idea that the scientist or the mathematician would need the philosopher to guide it’s research. Philosophy is a discipline of conceptual creation, sufficient by itself, as any other field. I think that he wants to emphasize it’s independence, as a pure creative field, like any other field. This conceptual factory is nonetheless fundamental, because it provides the new lens that will save us from a myopia in front of the the new realities that unfold in front of our eyes (which are, by the way, made of assemblages of mathematics, science….programming, among other things).
Cybernetics
Let’s get a short and broad definition of cybernetics:
“Cybernetics is a wide-ranging field concerned with circular causal processes such as feedback.” (source).
Here is a diagram that represents the phylogenesis of Wiener’s cybernetics:
Source: Systems Theory and Systems Analysis. Systems Engineering, Dmitry A. Novikov, Institute of Control Sciences, 2015
As Dmitry A. Novikov stated, it’s still difficult to provide a unique definition to cybernetics, as for any other “comprehensive category“. But Andrey Kolmogorov’s definition is quite interesting:
“A science concerned with the study of systems of any nature which are capable of receiving, storing, and processinginformation so as to use it for control.”
I personally like G. Bateson’s definition:
“A branch of mathematics dealing with problems of control, recursiveness, and information, focuses on forms and the patterns that connect.”
Through this post, I want to argue that programming is inherently a cybernetic process. I can already anticipate some individuals who are familiar with the concept claiming that programming is about constructing systems, which are inherently cybernetic.
While it is true that programming is about building systems, and systems are inherently cybernetic, my point is that programming is not solely a tool to build cybernetic systems; programming is itself a cybernetic act. Programming programs us as much as we think we program the system, if not more. Moreover, I want to emphasize that these systems consist of assemblage of Natural Language names, which holds significant power. In this sense, we can view the system as an extension of our “second self“‘ as coined by Sherry Turkle in her book “The Second Self: Computers and the Human Spirit“.
The Art of naming: more than aesthetics, a performative act
We want our creations to “stay” in general, don’t we?
Yet, what stays is what is transferred.
And, what is transferred, is what is commonly understood and shared.
As software size and complexity increase, so does the need to rightly name variables.
The act of transferring software (a bundle of named variables, among other things) – from person-to-person – is communication, but not only.
And documentation is often incomplete.
In this context, here is some food for thought:
The art of naming (variables) lies in finding the perfect balance between simplicity and significance.
This one digs deeper:
Naming things is essential, as it makes us aware of their existence.
As Albert Camus said: “To name things wrongly is to add to the misfortune of the world“. We could replace “world” by “software”.
Netscape’s (a web browser that played a significant role in the early days of the World Wide Web) Phil Karlton stated the following:
“There are only two hard things in Computer Science: cache invalidation and naming things.“
To name things wrongly is to add to the misfortune of Software
Marc Andreessen very famously stated that “Software Is Eating the World“.
I would add that Software is Eating the World because software is not only about “tech”. It’s about something deeper. “Tech” as we refer it commonly in our day-to-day life came after software existed.
Software is about decomposing problems, and naming the smaller parts
As professional developers, we create software as a response to a demand from society. But in return, software creation seems to unveil new problems, and developers name them as they build software.
There is a feedback loop (see cybernetics to delve deeper into this fascinating topic). It is precisely this feedback loop that I want to draw your attention to.
As the program grows in size – in complexity – the cognitive load increases, and then, we try to make our program more modular (at least, it’s what should be done). The DRY principle (Do Not Repeat Yourself) is a software philosophy that reigns among developers, and rightly so, as it participates to decrease this cognitive load. So is the goal of type hints, doc-strings and comments.
If we dig deeper, what lies behind the need for modularity, DRY, classes, abstractions,… is naming. By being pushed to generate abstractions, we are pushed to rethink variables, functions and class names. Problem decomposition asks for naming these new parts, and, when we are pushed to name these new parts, it makes us more aware of the deep nature of the problem.
At the end, the solution we invent – the new “System” – is a bundle of new names that are then crystallized – so to speak – into physics (silicon, energy transfers,…). From metaphysics to physics. Pardon my poetic drift.
Problem decomposition paves the way to new structures, that will constitute new Systems.
Abstractions make Maps: mental span increase and cognitive load reduction
What do I mean by “mental span“? I refer to the amount of computational representation one is capable of. To put it in other words, it is the amount of complexity (the number of connections) one is capable of representing in a certain amount of time.
To successfully navigate the world is about being able to focus on the right object, on the right abstraction. Otherwise, “things” would be overwhelming, unbearable. Imagine trying to focus on every single color, object, sound,… It is not only not advisable for your mental health, but it is impossible. Think of it as a map. A map is not the actual territory. It doesn’t show everything, but it shows us what is essential to get where we want to go.
And, abstractions exist only because they are named. Literally, you can’t declare a set of objects that have no name. In this sense, software is very interesting from a philosophical point of view. There is a link with a philosophy of spirit and a philosophy of language. If we leave the realm of programming to investigate our minds, what is a thought? Do unlabeled thoughts exist?
I will continue this article and build upon Norbert Wieners’ Cybernetics: or Control and Communication in the Animal and the Machine.
