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AI beyond the hype

AI has become the latest and biggest buzzword since the release of GPT 3.5.

This has inevitably created a hype that will eventually lead to some disappointment, given that most companies, both old and new, are rushing to ride the wave of AI to make a quick buck. All you need is a half-baked idea, a demo running as a wrapper around OpenAI's API, and the ear of an investor.

There is also considerable hype regarding the immediate effects of AI on the job market. Current AI is changing things, but humans are still needed in one way or another. It will take more time to reach the point where human work becomes obsolete and optional.

Putting the hype aside, there is something more important and more fundamental to consider beyond the commercial aspect of AI: the relatively simple structure of neural networks that can give rise to intelligence as we know it.

Humans have always fantasized about alternative life forms, perhaps somewhere outside our planet. However, life as we know it is extremely complex and requires a chain of fortuitous events, spanning millions or billions of years, to evolve from simple molecules to complex organisms.

We've also been assuming that intelligence is a property that derives from life, and finding intelligent life forms would mean we'd have to first find life, and then intelligent life on top of that. This notion is now challenged.

Artificial Neural Networks demonstrate that intelligence requires a relatively simple structure that doesn't need to be organic. Granted, today's LLMs (Large Language Models) are not self-sustaining, self-evolving, and self-replicating entities, but it's clear that those properties are likely to follow. We can already see hints of this with agent-based systems that rely on LLMs to go beyond the capacity of a single inference cycle (inputs -> model -> outputs).

Gone are all the theories about the soul, consciousness, and even quantum effects on neurons. We are now very close to creating entities that can think, learn, and evolve, and we can do so with a relatively simple structure.

In conclusion, I have no doubt that the commercial aspects of AI are being overhyped. However, it would be a waste to focus purely on that aspect, without considering the deeper significance that this holds... we have distilled intelligence, and it didn't require a magic formula or a metaphysical event to do so.

Somewhere in the universe, there may be other "engineer types" that have been forged from energy applied to structures like some form of crystals, or maybe some kind of fluid with enough mechanical properties to form and sustain a network of connections with its bits of memory and its processing units.

This would be an exciting prospect because it would mean that intelligence is less rare than we thought, given that it wouldn't require the complicated, long chain of events that life as we know it requires.

ENZO-TS 2024/Q1 update

This is a general update on the ENZO Trading System as of Q1 2024.

NOTE: We have been phasing out the trading client in favor of ByBit Copy Trading platform. The client is still supported for those that have it and for custom licenses.

Over the past year, significant developments have occurred behind the scenes. The decision to delay updates until we had a stable system worth discussing has culminated in our latest release in mid-March 2024.

This release was focused on stability, putting preservation of capital above everything else. This translates into less trading activity and less potential for profit, but at a much reduced risk level.

The system is now able to completely withdraw from all crypto markets when conditions are unfavorable, as opposed to simply shifting allocation to the least unfavorable markets. This was essential, because crypto markets offer virtually no diversification during "bear" phases, offering no hedge against losses other than halting trading altogether and holding cash (USDT in our case).

In practice, this required major work in rethinking of algorithms, modes of evaluation, and portfolio management. If one isn't careful, simply withdrawing from trading when things aren't going well can do more harm than good, as one may easily enter a cycle of racking losses and stopping trading just as the market is about to turn around.

The problems with trading

With sufficient GPU power, creating very capable artificial intelligence language models (LLMs) has become commonplace, so much so that LLMs are quickly becoming a commodity. Meanwhile, trading continues to be an unsolved problem, at least as far as public knowledge goes.

There's plenty of publicly available material on the subject of automated trading, but nothing seems to really work on its own. One can't simply download a trading model and immediately apply it to the markets for a profit. Here are a few reasons why:

  1. Trading is practically a zero-sum game: there's no incentive to share the details of a successful strategy as it could quickly become ineffective.
  2. The markets are constantly evolving: what works today may not work tomorrow.
  3. There's a high degree of randomness at play, making it difficult to distinguish between a great strategy, pure luck and anything in between.

In my experience, the vast majority of information circulating about trading is plagued by fundamental misconceptions and misunderstandings. The landscape is saturated with half-baked ideas and outright quackery. This situation is unlikely to change, as there is little financial incentive for individuals to share genuinely useful and practical information. In a field where edge is everything, those who possess valuable insights are more likely to guard them closely rather than broadcast them to the masses.

Improvements to the system

The two most significant changes for this release were:

  1. Introduction of Neural Networks for the production of new algorithms.
  2. Rethinking of the portfolio management, with extreme risk adversity in mind.

Of these, I believe portfolio management is the most critical change, though it may lack the allure of high-tech innovations.

The valuable Sharpe ratio

Assessing the performance of a trading system is more complex than it might appear. Over time, I have come to value metrics such as the Sharpe and Sortino ratios more than metrics like profit factor, win rate or profit adjusted for maximum drawdown.

The reason is that Sharpe/Sortino tend to favor stability. Stability is not only a desirable attribute for investments but also a key indicator of a system’s reliability and predictive powers. All recent changes were made with this in mind.

NOTE: Sharpe ratio is not a magical formula. What's important is to understand why it's valuable. A similar metric may work as well or better. In fact, ideally one should be able to replace Sharpe ratio with a similar metric and obtain similar results. Doing this would be a good test of the overall robustness of a system against overfitting.

About the new portfolio management

A crucial insight we gained was that effective portfolio management relies on the algorithms being as stable and predictable as possible. Paradoxically, an algorithm that brings consistent losses is preferable to one that is profitable but erratic.

This is because an algorithm that begins to underperform and continues to do so for a prolonged period of time, can quickly be disabled, knowing that more losses are likely to follow.

On the other hand, an algorithm that is known to be profitable but only because of a few trades that could happen at any time, is not suitable to be integrated in a portfolio. This is because it becomes much harder for the portfolio's algorithm to decide how to allocate capital to it, due to the random nature of this signal.

Implementing this required extensive research and rigorous testing. Part of our testing strategy now involves evaluating the system during the worst market periods. We assess performance in scenarios dominated by poorly performing "altcoins", without the safety net of more reliable assets like Bitcoin and Ethereum. The goal is to minimize losses and drawdowns as soon as a market shows signs of weakness.

This approach of extreme aversion to risk translates into overall much reduced trading activity, to the point that the system may be out of the market for long periods of time. This is much less exciting and rewarding for the short term, but it's what is necessary for a system that aims to survive and thrive in the long term.

High stability and low risk are not directly a measure of profitability, but they are essential to optimize the allocation of capital, and they open the door to trading with higher leverage, which ultimately leads to higher returns.

Neural networks and trading

The incorporation of neural networks into our trading system has been a long-awaited development. I personally decided to approach this transition cautiously, as it required extensive internal research, given the aforementioned lack of reliable information in the public domain.

After considerable research, I opted for a familiar and promising approach: a simple deep neural network optimized with neuroevolution (also known as genetic algorithms). The neuroevolution component is implemented in plain C++, while the neural network operates on LibTorch (the C++ API for PyTorch).

Neuroevolution is not what makes the news these days, but in cases like these, where one is optimizing for an outcome for which there isn't a well defined ground truth, it becomes important to explore the solution space extensively, and this is where neuroevolution shines.

Given more time, it would be interesting to give another look at backpropagation and transformers, but this will require a significant amount of research, as none of this is plug-and-play. The devil is absolutely in the details... potential is often hiding behind days of testing and tweaking with often no hint in sight.

Last but not least, I cannot overstate the importance of creating a suitable fitness function (or loss function) for the training. This is where large part of the research went, and it's what a human programmer still has to spend the time on, to essentially make it possible for a network to be trained without going off the rails.

A good fitness function should evaluate results in a realistic way, not expecting the network to do the impossible, as it will inevitably fail to produce anything useful. At the same time, one shouldn't ask too little of the network, as the threshold for usefulness is quite high. Trading is not an incremental job. A network needs to be very good at predicting before it can be useful at all. The cost of misprediction can be very high.

NOTE: A common misconception in trading, automated or not, is that one can "start small" and "just earn $50 a day" and grow from there. This couldn't be further from the truth. If you can make $50/day, you can make $5,000/day. It's not about how much you make, it's about earning anything at all, without going broke. The threshold for success is deceivingly high !

This in practice means that, for example, when it comes to a network that is supposed to predict the price of an asset, the expectation for prediction is in the order of hours rather than days, given that longer term predictions are much harder to make.

For more advanced networks, which generate trading signals themselves, the fitness function should focus on the stability of trades rather than profit. In this case however Sharpe ratio is not a good candidate, as it's very sensitive to nonsensical trades, something very common in the first stages of training.

Benefits of using neural networks

Neural networks are a powerful tool, but they aren't a blanket solution for all trading needs. In fact, some of the older algorithms are still active part of the system and are sometimes outperforming the algos based on neural networks.

The major advantage of NNs is that, for the most part, they remove the need for human intuition. This means that once the system to effectively train a neural network is in place (substantial but fixed initial cost), it can be used to create new algorithms that can adapt to new market conditions with minimal human intervention.

From this perspective, even if an NN-based algorithm performs worse than an hand-crafted one, it's still a very valuable tool, because it produces something valuable with very little extra human effort. The ratio between the outcome and the human effort is what matters, especially when it's one own's time that is being spent.

Some technical details on the neural network implementation

The code is in C++, using LibTorch (the C++ API for PyTorch). Substantial effort went to optimizing the code, both by parallelizing the training using higher dimensional tensors, and by using async transfers to the GPU, although this proved to be unreliable on my Macbook Pro with an M2 chip.

It seems that to reliably use async transfers (the non_blocking parameter in the function), one should use CUDA-specific features, but it probably makes more sense to move training to Python + PyTorch, including the neuroevolution part, which is supposedly doable with PyTorch itself. This is something to consider for the future.

Beyond that, backpropagation and transformers would be an even more interesting proposition, but actually making it all work will require an unclear amount of work doing the research.

I'm sure that large trading firms with the necessary resources have been doing this for years, but without any information it's hard to guess what it looks like and how valuable it is.


Since the start of this multi-year journey, I've many times felt like it was time to sit back, relax and enjoy the profits. However, this never truly materialized. I lost track of the times that I'd have to go back and tweak things and often just scrap something completely. This includes the constant search of useful methods of evaluation, due to the fact that there is no ground truth to evaluate trades, and it's even more difficult to evaluate the overall goodness of a system from the trades it produces.

The hope for this release is to have, first of all, a system that is stable and resilient to the worst market conditions including the very dangerous state after a bull run, where momentum in asset allocation can lead to rapid setbacks. The ability to decide when to withdraw from the market, in a timely manner, is as essential as anything in a trading system.

Secondly, I'm personally excited to have a NN-base model that can do the heavy lifting in trading. This not only means that we can quickly adapt to market changes and potentially look beyond crypto, but also that I hopefully will no longer have to spend so much time working as an algo-monkey, tweaking and tuning algorithms on an endless loop.

The irony of LLM hallucinations

The advent of LLMs (Large Language Models) has been nothing short of revolutionary. Building intelligence from text (and code), is something that I didn't think would be likely. One may argue about the essence of it, but the result is undeniable, and it's only a start.

We now have a seed of alien intelligence, and it's something that is improving possibly at an exponential rate. This is the real deal, but it comes with some flaws.
One common complaint about LLMs is that of the "hallucinations" that they can produce. An hallucination in this context is generated information that is patently untrue, presented without any hesitation. It's a kind of delivery that our human brain finds uncharacteristic of an intelligent being.

This is something that I think it's probably already fixable (see my ChatAI project) with some forms of cross-referencing, and it's not yet deployed due to resources required. I don't consider this to be a major issue for the future, but it's something that got me thinking...

I think that it's ironic how quickly we point the finger at the flaws of these systems, while at the same time we're so inherently flawed to such a depth that we don't yet fully realize it. As AI will improve, this will become more evident, and at some point we'll have to do some introspection and see if we can afford to go on as we have had so far.

Humans live in a bubble of total delusion, both at the individual and at the mass level. Our delusion is not simply an existential one, which would be a noble thing, but it's lower level than that: we lie to ourselves and to others on a constant basis due to tribalism and indoctrination that we receive from the day that we're born.

School, corporations, governments, religious groups, politicians, journalists, experts, scholars, you name it. There's a constant stream of delusional, selfish, malicious or clueless people that poison the well at the higher level, constantly crippling society.
Corruption, thirst for power, idealism, anything for which "the end justifies the means" is usually a sign that something is rotten and is going to hold back progress.

Perhaps we thought that in the information age things would get better, but what we got is information overload, and most of it is biased and purposely given to us to steer us in one direction or another.
The information age clearly didn't bring the sort of enlightenment that we may have hoped for, but perhaps the AI models (especially the open sourced ones) will start to help the individual to deal with the problem of information overload that has been crippling us.

Regardless, I think that we should be more humble when we criticize the flaws of the current AI models, and take that a as jumping point to do a little more introspection and realize how much we can and should improve ourselves.

I know that AI will improve. The question is whether we will improve as well.

Computer code, probably instrumental for AGI

Commodore VIC-20 (1981)

While I wouldn't consider myself an AI expert, I've been working with machine learning for a few years and have formed some understanding of the subject.

My journey with computers began with a Commodore VIC-20, attempting to communicate with it in natural language, inspired by the movie WarGames (1983). The initial disappointment from the inability to extract functionality from this tool turned into a challenge that led me to learn programming. Since then, I've had ample time to ponder logic, intelligence, problem-solving, and the essence of being intelligent and sentient, and just how far we were from creating something that could pass the Turing test. We're well past that now, and talking about AGI (Artificial General Intelligence) is legitimate, if not necessary.

Computer code as foundation for AGIs

A key realization for me has been the integral role of hands-on experience in developing a true understanding of a subject, and sometimes of a mindset. I've found that deep comprehension of complex topics often demands more than study; it requires building (often more than once) the subject matter in code. While learning styles vary, the act of creation undeniably deepens understanding. Even for those adept at absorbing knowledge from reading, the kind of comprehension that forms a foundation for further intellect often comes from practical engagement, where the journey to reach a goal is more important than the goal itself.

I believe this process of intellectual growth through implementation and creation is essential in building an AGI, and software development provides the ideal playground. Large Language Models (LLMs) have reportedly improved by learning from computer code, which is more structured than human languages. This suggests that code should remain a key resource for advancing AI systems.

An AI system with experience in building software would make a much better companion than one that can simply recall and implement things it's read about. This is akin to the difference between a wiz kid who can ace tests and a seasoned engineer who can guide you through every step of the process based on personal experience, highlighting not just the methods, but also the rationale behind them, while anticipating many of the pitfalls.

Software development has also a recursive component to it. In a previous article ("Next level thinking ?"), I mentioned how, in my opinion, recursive thinking is a fundamental building block of our intelligence.

The power of recursion here comes by way of being able to leverage software to build more complex software, as well as building the testbed for virtually any simulation that reflects the physical world. The more accurate the simulation, the less we need to rely on testing in the physical world. Testing in the physical world is not scalable, it can require a lot of resources and it's often destructive... imagine crash testing for the safety of a car: a fairly accurate simulation can drastically cut the requirement for testing. Simulation may be hard to implement, but it can allow to run a large amount of tests for a large number of configurations, more than it would be humanly possible (see also "Simulation: from weapon systems to trading").

Don't give up on programming just yet

I also think that while the ability to deal with human language is fantastic, communicating in computer code is probably more efficient in many cases. Often I ask LLMs to give me some pseudo code rather than a long explanation or a series of bullet points. Code can be more concise, it's definitely less ambiguous, and it's more of a direct building block to further knowledge and understanding.

For this reason, I also think that programming is not necessarily dead. Logical languages, such as computer languages, in many cases may become a better global language than English. Human languages are still important at the historical level, they are a reflection of what we are, but they are never going to be as efficient and precise as a rigidly structured language that runs in a digital environment.

It should be noted that OpenAI has recently introduced "Code Interpreter", which gives the ability to execute the generated Python code. As well as "function calling", which introduces pseudo-code as a way to obtain better structured answers. This level of efficiency can't possibly be replaced by human languages that maybe be a good interface between humans, but that are not very efficient nor precise when it comes to describe systems that are more analytical in nature.

A definition of reality

We should start from the premise that we operate from the point of view of self-preservation. This would be preservation of the immediate self, but also preservation of offspring and peers as a reflection, since they also represent a vehicle of the self into the future. Then, our existence is contingent on our ability to predict the future. The better the prediction, the better are the chances to stay alive and to project our own existence. "Will I be alive tomorrow if I stay into this cave ? Will I cripple myself if I walk barefoot on gravel ?"

Any prediction requires two elements:

1) Prior knowledge, the more the better.
2) Analytical tools to reliably derive an outcome from that prior knowledge.

To be efficient at this, we need to be able to process information in the most deterministic way possible. This means that the most valuable information is information that we can put through our analytical machinery (the brain, or any other sufficiently powerful calculator), and that can lead to a useful forecast of a given event.

The keyword here is "determinism", something that requires a level of rigidity that may be challenging for a human brain. This is actually where digital data storage, math and digital computers can help greatly.

The scientific method is also built on the concept of determinism. If you can consistently use prior knowledge to determine a future event, then your actions are useful and deserve more exposure. If instead a theory cannot predict the outcome, then the theory has no practical value. Of course the inability to prove something doesn't mean that a theory is necessarily invalid, but for all intents and purposes, one simply cannot rely on a formula that cannot forecast an outcome.

One could think of a deterministic structure as a web of points of determinism. A graph where each node is connected to other nodes. The more the connections, the more reliable is a certain fact established by that point of determinism. Nodes at the fringe of the web, with virtually no connections to others, must still exist, otherwise there would be no progress, but they are less reliable, because they have less support than the better interconnected points.

Image courtesy of Simon Cockell

The network structure is pervasive in nature, and it's how our brain is built. This structure is also what reality is to us: things are more or less real, depending on how well we understand them, meaning how well they relate to other pieces of data, with the goal of making useful predictions. The moment in which such structure comes less, it's also when reality dissipates. Reality is indeed a structure.

Going beyond definitions. Once we establish all this, it should be clear that to have any kind of success at any level, one is better served by leveraging determinism as much as possible (unless "success" is defined by self-destruction, but that's uninteresting and easily obtained).

In practice, establishing a robust level of determinism can be very difficult. The difficulty of establishing a clear logic connection between concepts can vary wildly. This depends on the amount of data at play and the process of analysis required to interpret those data. Most complex subjects are filtered through the lens of experts, which use their skills and experience to reduce complex subjects to a simplified form that can be easily digested by the non-expert. Experts however can often disagree on fundamental issues, hopefully in good faith, but sometimes in bad faith, too. On top of that, communication of the synthesized concepts usually goes through additional levels of simplification and bias. For larger issues, experts defer their communication to technical leaders, political leaders, public-relations, editors and finally journalists/writers.

Putting potential malice and communication issues aside, there's an inherent problem of complexity. Some fields necessarily rely on complicated statistical models to attempt to establish cause and effect while operating at a level of knowledge that is not very detailed.

Medical trials are a good example of an attempt to establish determinism from a very complex system (the human body) that in some cases one has to treat as a black box. A "black box" being a system that generates an output for some inputs, although the user doesn't completely understand the complex mechanics that make the box work. With medicine there may be a general understanding of the benefits of certain elements on the human body, but there is also hidden complexity of the system that forces researches to take a trial-and-error approach, usually starting from other, more expendable, living creatures.

The concept of "black box", stylized

Another problem with establishing determinism is that too much reality hurts. Suspension of disbelief is a necessary tool for survival. It's necessary against the nihilistic picture that naturally arises from thinking about existence. Imagination is essential both to cope with reality of existence, and to expand the vision of reality. At the same time, imagination can easily be abused to the point of becoming counterproductive.

In my opinion, while it's essential to keep a grasp on the better established aspects of reality, it's also important to let the mind run free, and to occasionally listen to "the crazies" (see "Adopt a conspiracy theorist"). This is a good mental exercise and also a reminder that reality is a dynamic concept. To push this to an extreme, in some ways, the person that cuts his arm looking for an alien microchip, may be closer to the universe than the person that wakes up at 8 in the morning to go to the office for 40 years. The schizophrenic has less ties to the common concept of reality, but our common concept of reality is also infinitesimally limited in the context of the greater universe.

A less extreme example of lateral thinking is that of religions. The modern educated man deride religions and the concept of a God, as some silly coping mechanism, however the fundamental questions that religions attempt to address never went away. One may decide to ignore those questions for a while, but they keep coming back. This is why nowadays theories about living in a simulation are so popular, it's just a modernized version of religions. It's a framing that has a better grounding with our map of reality, one that includes more advanced reasoning tools, but it's a framing that also validates the fundamental pursuit of ancient religions. In fact, the "simulation theory" could easily become the basis of new religions, where God is no longer an old man living in the clouds, but a game developer that clicks on you and checks your status from time to time.

"Little Computer People" on the C64

In conclusion, it's safe, efficient and productive to rely on the better established areas of the "reality network", but it's also important to keep in mind that there is a virtually infinite potential to explore and expand that structure, and that our point of view is extremely limited. Finding the right balance is the hard part, as with everything. Stay at the center and you're golden, but you're going to live a derivative life like any working bee, move too far to the edges, and life can become a mix of fantasy and paranoias. May the most balanced player win !

Below, a tribute to Terry A. Davis, a fellow programmer that was definitely living on the outer side of the network.

Simulation: from weapon systems to trading

When I first became interested in algorithmic trading, I didn't have any kind of trading experience. I did however have some very limited experience with flight simulation and weapons systems as an evolution from working in game development.

Although I didn't know this at the time, simulation is just about the most important thing when it comes to developing trading strategies.

In weapon systems, simulation allows to estimate how reachable a target is. This can be used either to extrapolate fundamental formulas to be applied in real-time, or the simulation itself can be executed in real-time, if a weapons system has enough mobile computing power.

In the first case, simulation can help to build tables of parameters for quick retrieval when a weapon needs to be used. Let’s say that one has a cannon with a new kind of ammo. A well-established analytical solution can be adapted for the new ammo. But for more complex cases (i.e. different aerodynamical profile of the projectile) a computer simulation can find a more precise solution. A machine learning system can try millions of possible variations of parameters, such as elevation of the gun and amount of propellant, find the best possible one for given distances, and build a table that can then be consulted when the time comes to use the cannon to hit a target at a specific location.

Below is a ballistic table used in World War 2:

WW2 ballistic tables courtesy of

Here's an interesting explanation of bomb drops procedure in Vietnam using a F-4, which didn't have a computer for the task.

In the second case, a more advanced weapon system, let’s say one in a modern jet fighter or in a modern tank, could forgo with most precalculations and instead have the simulation running live. The benefit of this second solution is that it can adapt to more variables and it can improve with a software upgrade.

In practice, setting up a simulation that is accurate enough to replace most of the experimental work, is not a simple thing, unless one is already developing a simulation, such as a flight simulation game. When the time came to build a believable weapons system in my experimental flight sim, it was only natural to use the simulation logic already developed for the game to calculate the launch envelope for a bomb or a missile, or the homing guidance for a missile.

Why manually extract a formula, when you can simply use the already existing simulation engine to test thousands of trial-runs and find the best parameters for the optimal launch envelope ? This was my epiphany in regard to the power of building a simulation to solve problems in a more generic and simpler way. Once you have a simulation for something, then you can use raw computing power to search for the best variables, whether that’s to built a table to consult at a later time, or whether it’s to be used actively up to the point of launch of a projectile, or even during flight of a guided missile.

Practical application of simulation in the real world is probably something in between. Supercomputers can be used to analyze more complicated matters such as the aerodynamics of a particular kind of fins of a missile, while on-board computers can work on launch envelopes and homing guidance. For sure, once a problem is shifted into software, chances for improvement increase sensibly.

This does connect neatly to algorithmic trading and how fundamentally dependent it is (or it should be) on the concept of simulation. No sane person should ever attempt to trade without first having a computer model. Some may disagree, but I can’t imagine how having an analytical model would hurt success.

The only drawback would be the issue of the reliability of a computer model of something that is very stochastic in nature. A weapon has little room for error. Simulating a projectile may be difficult, but the outcome is immediate and straightforward. With trading, instead, success can only be found in odds with large numbers in an highly dynamic setting. This makes the process of optimization more difficult and often counterproductive, but this is a discussion for another post.

Women and children

In 2022, war is now truly a live event. With cell phones, body cams and quad-copter drones, social media is filled with an abundance of the most disturbing footage that one can imagine.

This is certainly shocking from the perspective of societies that have lowered the bar for conflict to that of verbal micro-aggressions. At the same time, this is also very educative. It's a perspective into what humans are and can be (see also "The explosive 'boredom' problem of civilized societies").

I see interviews of people living underground and struggling simply to stay alive and find water and food, and I think of what I pictured from the WW2 stories from my grandparents.

Image courtesy of Alexander Turnbull Library

Everything checks… that's the war I've been hearing about, a war where human cost is obvious, one that feels close enough, because it's not happening to the third world. We're used to ignore the homeless in the streets, but if it's someone that looks like he has a job like us, then that's a different matter. Affinity with race also helps a lot to mobilize millions of voices on Twitter. As much as we can pretend that we don't care, we all instinctively feel more empathy towards living beings that look more like us. Our level of empathy and altruism varies in a continuous scale from two extremes, where most affinity is generated by our own children, and least affinity is probably some sort of alien insect coming from another galaxy.

Another major reality check from this war is how biological gender suddenly matters. Although some women are involved, they are very few and are usually relegated to being snipers, the kind of activity that requires less direct conflict. Most women are instead grouped with children and elders, while men are actually forced to go in combat, at least from the Ukrainian side. I think that we can safely assume that when shit truly hits the fan, the gender equity construct that has evolved in western societies goes straight into the pooper.

One could say that if there were no wars, then men and women could almost become interchangeable, but to expect a natural world without conflict is even more utopic than to expect a natural world without biological predispositions. Those that seek total equity for humanity have a better chance looking for it in the after-life, or in some fictional far future where any form of individualism is crushed together with all major human traits that make us what we are.

Meanwhile, in the real world, it would be wise to keep in touch with reality, because sooner or later the protective shield under which we are born and raised, will falter, and at that point understanding human nature will become a matter of survival.

An actual phobia, for a change

I'm not a fan of the "phobia" suffix. I think that it's easily abused these days.

One classical misuse of "phobia" is with the term "homophobia", as if straight men had nightmares of being chased at night by a cosmopolitan gay man holding a pair of sharp scissors !

Jokes aside, the presumed fear in this case would be that of the potential for straight men to find out that they may actually be attracted to the same sex. The idea is that it would be some sort of fear of the unknown. Calling someone an homophobe is an attempt to paint that person as unrefined and uncultured, someone that dismisses same-sex intercourse instinctively, someone that has not evolved to the point of going beyond his natural instincts, and that is afraid of doing so.

That is some cheap psychology that unfortunately has become mainstream and has indeed helped to put straight people on the defensive. This is the kind of propaganda that drives many men to pledge some kind of allyship just to look more socially aware.

Now for an example of an actual phobia, these days I'd put Russophobia on top. I'm impressed by the amount of people, especially in the United States, but apparently also in some countries in Europe, that suddenly appear to be fanatically anti-Russian. The 80s are back, with a vengeance !

The reason why I think that this is a legitimate phobia, is because the hate that is displayed is borne out of actual fear. Deep inside, these people realize that Russia is a force to be reckoned with. Russia is a country that even after the fall of the Soviet Union has continued to develop its own technology. This is most notable with aerospace engineering and weapon systems. I'm a big proponent of developing your own technology. In my case that would be software technology, but that's truly essential when it comes to tech that is vital to the security of a nation.

At the same time, the Russian culture hasn't been as softened as in the west. The average Russian is culturally more resilient to the concept of a full-scale war. The human cost that Russia is paying is unthinkable for the average American or European citizen. This is shocking for a population that has been so detached from conflict and that has a relatively comfortable life. A life so comfortable to the point in which public discourse has shifted towards complicated social and environmental issues that are not as consequential as they are made out to be.

Photo by Matt Hrkac/Flickr, CC BY-ND

Perhaps the most important source of this phobia is the realization that the current generation of US and Europe military have little experience with conflicts that are not asymmetrical. Simply put, America has been picking fights with 3rd world countries, bombing them to smithereens with virtually no opposition, and with no real repercussion for civilian casualties. I'm not going to say that any war is easy, but I'm sure that it's a lot scarier when your opponent also has tanks, jets, drones, cruise missiles and everything in between.

It should be noted that technology isn't simply about developing the smartest bomb or the jet with the coolest AR helmet. Technology is also essential in optimizing production of such weapons, as well as building systems that are resilient and don't require too much maintenance. F-22s and F-35s are fantastic on paper, but they are very expensive to produce and they tend to waste precious time on the ground for continuous maintenance. I'm sure that a similar argument can be made for other vehicles of the west vs those that are Russian-made.

The American and European population is scared of an adversary that isn't from the 3rd world. Meanwhile, the "emperors" that pull the strings of those respective regimes, are afraid to be found out as having no clothes. The survival of the elites of the west depends on the population to believe that those elites are virtuous, strong and powerful, but at the same time they cannot ask to their people to accept casualties, because they now live in a soft world, a world where every dead service...person is a tragedy.

The fear is real, and it's not misplaced. Hopefully the people will move from the stage of fear into one of acceptance, and will perhaps wake up from whatever dream they've been pulled into. Unfortunately it's in the human nature to wake up only when it's too late. A lot of people will have to suffer very grave conditions before they finally stop believing the cheap Hollywood action-flick narrative that they have been fed for decades.