evolution as a physical theory

Evolution has two ingredients, constant variation and constant selection.

Two important observations:

1. Variation in biology exhibits itself in myriad forms, but they all can be traced back to the second law of thermodynamics, which says that entropy (on average) always increases over time. (It is not a coincidence that Darwin formulated the theory of natural selection in 1850s, around the same time Clausius formulated the second law.)

2. If you decrease selection pressures, the fitness landscape expands. You see less people dying around you, but you also see more variety at any given time. As we learn to cure and cope with (physical and mental) disorders using advances in (hard and soft) sciences / extend our societal safety nets further / improve our parenting and teaching techniques, more and more people stay alive and functional to go on to mate and reproduce. Progress creates more elbow room for evolution so that it can try out even wilder combinations than before.

   Conversely, if you increase selection pressures, the fitness landscape contracts, but in return the shortened life cycles enable evolution to shuffle through the contracted landscape of possibilities at a higher speed.

   Hence, selection pressure acts like a lever between spatial variation and temporal variation. Decreasing it increases spatial variation and decreases temporal variation, increasing it decreases spatial variation and increases temporal variation.

These observations imply respectively the following:

1. Evolution never stops since the second law of thermodynamics is always valid.

2. Remember, Einstein discovered that space and time by themselves are not invariant, only spacetime as a whole is. Similarly, evolution may slow down or speed up in space or time dimensions, but is always a constant at spacetime level. In other words, the natural setting for evolution is spacetime.

It is not surprising that thermodynamics has so far stood out as the odd ball that can not be unified with the rest of physics. Principle of entropy seems to be only half the picture. It needs to be combined with the principle of selection to give rise to a spacetime invariant theory at the level of biological variations. In other words, evolution (i.e. principles of entropy and selection combined together) is more fundamental than thermodynamics from the point of view of physics.

Side Note: The trouble is that the principle of selection is a generative, computational notion and does not lend itself to a structural, mathematical definition. However the same can also be said for the principle of entropy, which looks quite awkward in its current mathematical forms. (Recall from the older post Biology as Computation that biology is primarily driven by computational notions.)

All of our theories in physics, except for thermodynamics, are time symmetric. (i.e. They can not distinguish the past from the future.) Second law of thermodynamics, on the other hand, states that entropy (on average) always increases over time and therefore can (obviously) detect the direction of time. This strange asymmetry actually disappears in the theory of evolution, where something emerges to counterbalance the increasing entropy, namely increasing control.

Side Note: Entropy is said to increase globally but control can only be exercised locally. In other words, control decreases entropy locally by dumping it elsewhere, just like a leaf blower. Of course, you may be wondering how, as finite localized beings, we can formulate any global laws at all. I share the same sentiment because, empirically speaking, we can not distinguish a sufficiently large local counterbalance from a global one. Whenever I talk about the entropy of the whole universe, please take it with a grain of salt. (Formally speaking, thermodynamics is not even defined for open systems. In other words, it can not be globally applied to universes with no peripheries.) We will dig deeper into the global vs local dichotomy in Section 3. (Strictly speaking, thermodynamics can not be applied locally neither since every system is bound to be somewhat open due to our inability to completely control its environment.)

1. Increasing Control

All living beings exploit untapped energy sources to exhibit control and influence the future course of their own evolution.

Any state that is not lowest-energy can be considered semi-stable at best. Eventually, by the second law of thermodynamics, every such state evolves towards the lowest-energy configuration and emits energy as a by-product. By “untapped energy sources” I mean such extractable pockets of energy.

So, put more succinctly, all living beings harness entropy to reduce entropy.

The accumulative effect of their efforts over long periods of time has so far been quite dramatic indeed: What basically started out as simple RNA-based structures floating uncontrollably in oceans eventually turned into human beings proposing geo-engineering solutions to the global climate problems they themselves have created.

Let us now look at two interesting internal examples.

1.1. Cognitive Example

Our brains continuously make predictions and proactively interpolate from sensory data flow. In fact, when the higher (more abstract) layers of our neural networks lose the ability to project information downwards and become solely information-receivers, we slip into a comatose state.

Our predictive mental models slowly decay due to entropy (That is why blind people gradually lose their abilities to dream.) and are also at constant risk of becoming irrelevant. To address these problems, our brains continuously reconstruct the models in the light of new triggers and revise them in the light of new evidence. If they did not exercise such self-control, we would be stuck in an echo chamber of slowly decaying mental creations of our own. (That is why schizophrenic people gradually lose touch with reality.)

Autism and schizophrenia can be interpreted as imbalances in this controlled hallucination mechanism and be thought of as inverses of each other, causing respectively too much control and too much hallucination:

Aspects of autism, for instance, might be characterized by an inability to ignore prediction errors relating to sensory signals at the lowest levels of the brain’s processing hierarchy. That could lead to a preoccupation with sensations, a need for repetition and predictability, sensitivity to certain illusions, and other effects. The reverse might be true in conditions that are associated with hallucinations, like schizophrenia: The brain may pay too much attention to its own predictions about what is going on and not enough to sensory information that contradicts those predictions.

Jordana Cepelewicz - To Make Sense of the Present, Brains May Predict the Future

1.2. Genomic Example

Since only 2 percent of our DNA actually codes for proteins, the remaining 98 percent was initially called “junk DNA” which later proved to be a wild misnomer. Today we know that this junk part performs myriad of interesting functions.

For instance, one thing it does for sure is to insulate the precious 2 percent from genetic drift by decreasing the probability of a mutation event to cause critical damage.

Side Note: It is amazing how evolution has managed to diminish the coding region down to 2 percent (without sacrificing any functionality) by getting more and more dexterous at exposing the right coding regions (for gene expression) at the right time. This has resulted in greater variability of gene expression rates across different cellular contexts.

Remember (from our previous remarks) that if you decrease selection pressure, spatial variation increases and temporal variation decreases. Nature achieves this feat via an important intermediary mechanism. To understand this mechanism, first observe the following:

1. Ability to decrease selection pressure requires greater control over the environment and decreased selection pressure entails longer life span.

2. Exerting greater control over the environment requires more complex beings.

3. More complexity and longer life span entail respectively greater fragility towards and longer exposure-time to random mutation events.

4. This increased susceptibility to randomness in turn necessitates more protective control over genomes.

Since an expansion in the fitness landscape is worthless unless you can roam around on it, greater control exerted at phenotypical level is useless without greater control exerted at genotypical level. In other words, as we channel the speed of evolution from the temporal to the spatial dimension, we need to drive more carefully to make it safely home. From this point of view, it is not surprising at all that the percentage of non-coding DNA of a species is generally correlated with its “complexity”.

I used quotation marks here since there is no generally-agreed-upon, well-defined notion of complexity in biology. But one thing we know for sure is that evolution generates more and more of it over time.

2. Increasing Complexity

Evolution is good at finding out efficient solutions but bad at simplification. As time passes by, both ecosystems and their participants become more complex.

Currently we (as human beings) are by far the greatest complexity generators in the universe. This sounds wildly anthropocentric of course, but when it comes to complexity, we are really the king of the universe.

2.1 Positive Feedback between Control and Complexity

Control and complexity are more or less two sides of the same coin. They always coexist because of the following strong positive feedback mechanism between them:

• Greater control for you implies more selection pressure for everyone else. In other words, at the aggregate level, greater control increases selection pressure and thereby generates more complexity. (This observation is similar to saying that greater competition makes everyone stronger.)

• How can you assert more control in an environment that has just become more complex? You need to increase your own complexity so that you can get a handle on things again. (This observation is similar to saying that human brain will never be intelligent enough to understand itself.)
  
  
  

2.2. Positive Feedback between Higher and Lower Complexity Levels

All ecological networks are stratified into several levels:

• Internally speaking, each human being is an ecology onto himself, consisting of ten of trillions of cells, coexisting with equally many cells in human bacterial flora. This internal ecology is stratified into levels like tissues, organs and organ systems.

• Externally speaking, each human being is part of a complex ecology that is stratified into many layers that cut across our relationships to each other and to the rest of the biosphere.

Greater complexity generated at higher levels like economics, sociology and psychology propagates all the way down to the cellular level. Conversely, greater complexity generated at a very low level affects all the levels sitting above it. This positive feedback loop accelerates total complexity generation.

Two concrete examples:

• The notion of an ideal marriage has evolved drastically over time, along with the increasing complexity of our lives. Family as a unit is evolving for survival.

• Successful people at the frontiers of science, technology, business and art all tend to be quirky and abnormal. (Read the older blog post Success as Abnormality for more details.) Through such people, an expansion of the fitness landscape at the cognitive level propagates up to an expansion at the societal level.
  
  
  

2.3. Positive Correlation between Fragility and Complexity Level

Overall fragility increases as complexity levels are piled up on top of each other. In order to ensure stability, it is necessary for each level to be more robust than the level above it. (Think of the stability of pyramid structures.)

Invention of nucleus by biological evolution is an illustrating example. Prokaryotes (cells without nucleus) are much more open to information (DNA) sharing than the eukaryotes (cells with nucleus) which depend on them. This makes them simpler but also more robust.

It could take eukaryotic organisms a million years to adjust to a change on a worldwide scale that bacteria [prokaryotes] can accommodate in a few years. By constantly and rapidly adapting to environmental conditions, the organisms of the microcosm support the entire biota, their global exchange network ultimately affecting every living plant and animal.

Microcosmos - Lynn Margulis & Dorion Sagan (Page 30)

Whenever you see a long-lasting fragility, look for a source of robustness level below. Just as our mechanical machines and factories are maintained by us, we ourselves are maintained by even more robust networks. Each level should be grateful to the level below. 

Side Note: AI singularity people are funny. They seem to be completely ignorant about the basics of ecology. Supreme AI will be the single most fragile form of life. It can not take over the world. It can merely suffer from an illusion of control, just like we do. You can not destroy or control what is below you in the ecosystem. Survival of each level depends on the freedom of the level below. Just like we depend on the stability provided by freely evolving and information exchanging prokaryotes, supreme AI will depend on the stability provided by us.

2.4. Positive Correlation between Fragility and Firmness of Identity

How limited and rigid life becomes, in a fundamental sense, as it extends down the eukaryotic path. For the macrocosmic size, energy, and complex bodies we enjoy, we trade genetic flexibility. With genetic exchange possible only during reproduction, we are locked into our species, our bodies, and our generation. As it is sometimes expressed in technical terms, we trade genes "vertically" - through the generations - whereas prokaryotes trade them "horizontally" - directly to their neighbors in the same generation. The result is that while genetically fluid bacteria are functionally immortal, in eukaryotes sex becomes linked with death.

Microcosmos - Lynn Margulis & Dorion Sagan (Page 93)

Biological entities that are more protective of their DNA (e.g. eukaryotes whose genes are packed into chromosomes residing inside nuclei) exhibit greater structural permanence. (We had reached a similar conclusion while discussing the junk DNA example in Section 1.2.) Eukaryotes are more precisely defined than prokaryotes, so to speak. Degree of flexibility correlates inversely with firmness of identity.

Firmer the identity gets, the more necessary death becomes. In other words, death is not a destroyer of identity, it is the reason why we can have identity in the first place. I suggest you to meditate on this fact for a while. (It literally changed my view on life.)

• The reason why we are not at peace with the notion of death is that we are still not aware of how challenging it was for nature to invent the technologies necessary for maintaining identity through time.

• Fear of death is based on the ego illusion, which Buddha rightly framed as the mother of all misrepresentations about nature. This is the story of a war between life and non-life, between biology and physics, not you against the rest of the universe or your genes against other genes.
  
  
  

3. Physics vs Biology

Physics and biology (with chemistry as the degenerate middle ground) can be thought of as duals of each other, as forces pulling the universe in two opposite directions.

Side Note: Simple design is best done over a short period of time, in a single stroke, with the spirit of a master. Complex design is best done over a long period of time, in small steps, with the spirit of an amateur. That is essentially why physics progresses in a discontinuous manner via single-author papers by non-cooperative genius minds, while biology progresses in a continuous manner via many-author papers by cooperative social minds.

3.1. Entropy, Time and Scale

Note that entropy and time are two sides of the same coin:

• Time is nothing but motion. Time without any motion is not something that mortals like us can fathom.

• All motion happens basically due to the initial low-entropy state of the universe and the statistical thermodynamic evolution towards higher entropy states. (Universe somehow began in a very improbable state and now we are paying the “price” for it.) In other words, entropy is the force behind all motion. It is what makes time flow. The rest of physics just defines the degrees of freedom inside which entropy can work its magic (i.e. increase the disorder of the configuration space defined by the degrees of freedom), and specifies how time flow takes place via least action principles which allows one to infer the unique time evolution of a particle or a field from the knowledge of its beginning and ending states.

Side Note: It is not a coincidence that among all physics theories only thermodynamics could not be formulated in terms of a least action principle. Least action principles give you one dimensional (path) information that is inaccessible by experimentation. Basically, each experiment we do allows us to peak at the different time slices of the universe, and each least action principle we have allows us to view each pair of time slices as the beginning and ending states of a unique wholesome causal story. (We can not probe nature continuously.) Entropy on the other hand does not work on a causal basis. (If it did, then it could not be responsible for time flow.) It operates in a primordially acausal fashion.

When we flip the direction of time, thermodynamics starts working backwards and the energy landscape turns upside down. Time-flipped biological entities start harnessing order to create disorder, which is exactly what physics does.

The difference between physics and time-flipped biology is that former operates globally and harnesses the background order that originates from the initial low-entropy state of the universe and latter harnesses local patches of order created by itself. (This is why watching time-flipped physics videos is a lot more fun than watching time-flipped biology videos.)

Side Note: There are nano scale examples of biology harnessing order to create disorder. This is allowed by the statistical nature of the second law of thermodynamics which says that entropy increases only on average. Small divergences may occur over short intervals of time. Large divergences too may occur but they require much longer intervals of time.

The heart of the duality between physics and biology lies in this “global vs local” dichotomy which we will dig deeper in the next section.

It is worth reiterating here the fact that entropy breaks symmetries in the configuration space, not in geometric one. (It may even increase local order in geometric space by creating symmetric arrangements, as in spontaneous crystallisation, which disorders the momentum component of the configuration space via energy release.) Hence, strictly speaking, the “global vs local” dichotomy should not be interpreted purely in spatial terms. What time-flipped biology does is to harness local patches of configurational order (i.e. degrees of freedom associated with those locations), not spatial order.

Side Note: Entropy also triggers the breaking of some structural symmetries along the way. According to inflation theory, as the universe cooled and expanded from its initial hot and dense state, the primordial force split into the four forces (Gravitational, Electromagnetic, Weak Nuclear and Strong Nuclear) that we have today. (Again, as mentioned before, entropy is an odd ball among all physics theories and is not regarded as a force since it does not have an associated field etc.) This de-unification happened through a series of three spontaneous symmetry breakings, each of which took place at a different temperature threshold.

3.2. Entropy and Dynamical Scale Invariance

Imagine a very low-entropy universe that consists of an equal number of zeros and ones which are neatly separated into two groups. (This is a fantasy world with no forces. In other words, the only thing you can randomize is position. So the configuration space just consists of the real space since there are no other degrees of freedom.) Global uniformity of such a universe would be low, since there will be only fifty percent probability that any two randomly chosen local patches will look like each other. Local uniformity on the other hand would be high, since all local patches (except for those centered at the borderline separating the two groups) will either have a homogenous set of zeros or a homogenous set of ones.

Entropy can be seen as a local operator breaking local uniformities in the configuration space. Over time, the total configuration space starts to look the same no matter how much you zoom in or out. In other words, the universe becomes more and more dynamically scale invariant.

Note that entropy does not increase uniformity. It actually does the opposite and decreases uniformity across the board so that the discrepancy between local and global uniformity disappears. Close to heat death (maximum theoretical entropy), no two local patches in the configuration space will look like each other. (They will be random in different ways.)

Side Note: Due to the statistical nature of the second law of thermodynamics, universe will keep experiencing fluctuations to the very end. It can get arbitrarily close to heat death but will never actually reach it. Complete heat death means end of physics altogether.

Now a natural question to ask is whether there could have been other ways of achieving scale invariance? The answer is no and the blocker is an information problem. You can not have complete knowledge about the global picture without infinite energy at your disposal and without this knowledge you can not define a local operator that can achieve scale invariance. For instance, going back to our initial example, if your region of the universe happens to have no zeros, you would not even be able to define an operator that takes zeros into consideration. All you can really do is to just ask every local patch to scatter everything so that (hopefully) whatever is out there will end up proportionally in every single patch. Of course, this is exactly what entropy itself does. (It is this random, zero knowledge mechanism which gives thermodynamics its acausal nature.)

Biology on the other hand creates low entropy islands by dumping entropy elsewhere and thereby works against the trend towards dynamical scale invariance. It is exactly in this sense that biology is anti-entropic. Entropy is not neutralized or cancelled, instead it is deflected through a series of brilliant jiu jitsu strokes so that it defeats its own goal.

Physics fights for dynamical scale invariance by breaking local uniformities in the configuration space and biology fights against dynamical scale invariance by creating local uniformities in the configuration space. This is the essence of the duality between physics and biology, but there is a slight caveat: Physics works on a global scale and hails down on all local uniformities in an indiscriminate manner, while biology begins in some local patches in a discriminate manner and slowly makes its way up to global scale, conquering physics from inside out, pushing entropy to the peripheries. (Biology needs to be discriminative since only certain locations are convenient to jumpstart life, and it needs to learn since - unlike physics - it does not have the privilege of starting global.)

Let us now scroll all the way to the end of time to see what this duality means for the fate of our universe.

3.3. Ultimate Fate of the Universe

There is no current scientific consensus about the ultimate fate of the universe. Some cosmologists believe in the inexhaustible expansion and the eventual heat death, some others believe in the unavoidable collapse and the subsequent bounce. Since nobody has any idea about how dark energy, dark matter and quantum gravity actually work, everything is basically up grabs.

Side Note: Dark energy is uniformly-distributed and non-interacting. It is posited to be the driving factor behind the acceleration of the uniform expansion of space. Dark matter on the other hand is non-uniformly-distributed and gravitationally-attractive. Together dark energy and dark matter make up around 95 percent of the total energy content of the universe. Hence the reason why some people call junk DNA, which make up 98 percent of human genome, as the dark sector of DNA. Funnily enough, in a similar fashion, more than 90 percent of the more evolved (white matter) part of the human brain is composed of non-neuron (glial) cells . (Neurons in the white matter, as opposed to those in the gray matter, are myelinated and therefore conduct electricity at a much higher speed.) It seems like the degree of complexity of an evolving system is directly correlated with the degree of dominance of the modulator (e.g. non-neuron cells, non-coding DNA) against the modulated (e.g. neurons cells, coding DNA). Could the prevalence of the dark sector be interpreted as an evidence that physics itself is undergoing evolution? (Note that, in all cases, the scientific discovery of the modulator occurred quite late and with a great deal of astonishment. Whenever we see a variation exhibiting substructure, we should immediately suspect that it is modulated by its complement.)

One thing that is conspicuously left out of these discussions is life itself. Everyone basically assumes that entropy will eventually win. After all even supermassive black holes will inevitably evaporate due to Hawking radiation. Who would give a chance to a phenomenon (like life) that is close to non-existent at the grand cosmological scales?

Well, I am actually super optimistic about the future of life. It is hard not to be so after one studies (in complete awe) how far evolution has progressed in just a few billion years. Life is learning at a phenomenal speed and will figure out (before it gets too late) how to do cosmic-scale engineering.

Since no one really knows anything about the dynamics of a cosmic bounce (and how it interacts with thermodynamics), let us finish this long blog post with some fun speculations:

• The never ending war between physics and biology may be the reason why time still exists and the universe still keeps on managing to collapse on itself while also averting a heat death. Life could have learned how to engineer an early collapse before a heat death or how to prevent a heat death long enough for a collapse. Life could have even learned how to leave a local fine-tuned low-entropy quantum imprint so that it is guaranteed to reemerge after the big bounce.

• What if life always reaches total control in the sense of Section 1 in each one of the cosmic cycles and becomes indistinguishable from its environment? Could the beginning state of this universe’s physics be the ending state of the previous universe’s biology? In other words, could our entire universe be an extremely advanced life form? Could this be the god described by Pantheists? Was Schopenhauer right in the sense that the most fundamental aspect of reality is its primordial will to live? Is the acausal nature of thermodynamics a form of pure volition?

intergenerational cycles in parenting

• Children who choose to do their own thing and nevertheless have good relationships with their parents often have open-minded parents whose own parents were oppressive. In other words, the freedom these children enjoy derive from the freedom their parents could not enjoy. Now that these children are growing up in a free environment, they will probably not give the same luxury to their own children since they are not even cognisant of its value.

• The fact that girls get more attached to their fathers and sons to their mothers creates a strange sort of justice between genders. If a father fucks up, then his girl grows up to fuck up as a mother, then her boy grows up to fuck up as a father and so on.

future of pharmaceutical industry

What will the future of pharmaceutical industry look like?

It is clear that we are reaching one end of a paradigm, but what most people still do not get is how big the oncoming changes will be. We are on the cusp of a great intellectual revolution, on par with the revolution in 20th century physics. Computer science is unlocking biology, just like mathematics unlocked physics, and the consequences will be huge. (Read this older post for a deeper look at this interesting analogy between analogies.)

For the first time in history, we are engineering solutions from scratch rather than stumbling into them or stealing them from nature. Western medicine is only now truly taking off.

Not only will this transformation be breathtaking, but it will also be unfolding at a speed much faster than we expect. As biology becomes more information theoretical, pharmaceutical industry will become more software driven and will start displaying more of the typical dynamics of the software industry, like faster scaling and deeper centralization and modularization.

Of course, predicting the magnitude of change is not the same thing as predicting how things will actually unfold. (Sometimes I wonder which one is harder. Remember Paul Saffo: “We tend to mistake a clear view of the future for a short distance.”) Let us give a try anyway.


1. Splitting and Centralization of the Quantitative Brain

Just like the risk analytics layer is slowly being peeled out of big insurance companies as it is becoming more quantitative (small companies could not harbor such analytics departments anyway), the quantitative layer of the drug development process will split out of the massive pharmaceutical companies. (Similarly, in the autonomous driving space, companies like Waymo are licensing out self-driving technologies to big car manufacturers.)

Two main drivers of this movement:

• Soft Reason. Culturally speaking, traditional (both manufacturing and service) companies can not nurture software development within themselves. Big ones often think that they can, but without exception they all end up wasting massive resources to realize that it is not a matter of resources. Similarly, they always end up suffocating the technology companies they acquire.

• Hard Reason. Unlike services and manufacturing, software scales perfectly. In other words, the cost of reproduction of software is close to nil. This leads to centralization and winner-takes-all effects. (Even within big pharmas bioinformatics and IT departments are centralized.) Software developed in-house can never compete with software developed outside, which serves many customers, takes as input more diverse use cases and improves faster.

Study of complex systems (which biology is an example of) is conducted from either a state centric or process centric perspective, using either statistical (AI driven) or deterministic (algorithm driven) methods. (Read this older post for a deeper look at the divide between state and process centric perspectives.)

In other words, the quantitative brain in biology will be centralized around four different themes:

1. Algorithm Driven + State Centric

2. AI Driven + State Centric

3. Algorithm Driven + Process Centric

4. AI Driven + Process Centric

Xtalpi is a good example for the 4th category. Seven Bridges in its current form belongs to the 1st category. There are other examples out there that fit neatly into one of these categories or cut across a few. (It is tough to cut across both state centric and process centric perspectives since latter is mostly chemistry and physics driven and tap into a very different talent pool.)


2. Democratization and Commodification of Computation

Big pharma companies could afford to buy their own HPCs to run complex computations and manage data. Most are still holding onto these powerful clusters, but they are all realizing that this is not sustainable for two main reasons:

• They either can not accommodate bursty computations or can not keep the machines busy all time. So it is best for the machines to be aggregated in shared spaces where they are maintained centrally.

• Since data size is exploding doubly exponentially, it is becoming harder to move and more expensive to store. (Compute needs to go where data is generated.)

Cloud computing took off for reasons entirely unrelated to biomedical data analysis, which will soon be the biggest beneficiary of this revolution as biomedical data sizes and computation needs surpass everything else. (It is not surprising that the centralized disembodied brain is developing in the same way as our decentralized embodied brains did. It got enlarged for social reasons and deployed later for scientific purposes.) Small biotechs can now run complex computations on massive data repositories and pay for computation just like they pay for electricity, only for the amounts they use. Big pharmas too are migrating to the cloud, finally coming to terms with the fact that cloud is both safer and cheaper. They are no longer uncomfortable departing with their critical data and no longer ignorant about the hidden costs of maintaining local hardware.

Long story short, democratization of computation is complete (aside from some big players with sunk cost investments) and the industry has already moved on to its next phase. Today we are witnessing a large scale commoditization of cloud services, driven by the following two factors:

• Supply Side. Strong rivals arriving and catching up with Amazon Web Services.

• Demand Side. Big players preferring to be cloud agnostic and supporting multi-cloud.

3. Democratization, Uniformization and Centralization of Data

Democratization. Big pharmas are hoarding data. They are entering into pre-competitive consortiums and forming partnerships with or buying diagnostics companies straight out. Little pharmas (startup biotechs) are left out of this game, just as they were left out of the HPC game. But just like Amazon democratized computing, National Institutes of Health (NIH) is now trying to democratize data. (Amazon and NIH are playing parallel roles in this grand story. Interesting.) Sooner or later public data will outstrip private data simply because health is way too important from a societal point of view.

Uniformization. NIH is also trying to uniformize data structures and harmonize compliance and security standards across the board, so that data can flow around at a higher speed.

Centralization. NIH not only wants to democratize and uniformize data, but it also wants to break data silos. Data is a lot more useful when it all comes together. (Fragmentation problem is especially acute in US.) Similarly, imagine if everyone could hold all of their health data on a blockchain that they can share with any pharma in return for a compensation. This is another form of centralization, radically bringing together everything at an individual level. All pharma companies need to do is to take a cross section across the cohorts they are interested in.

With its top-down centralized policy making and absence of incumbent (novel drug developing) big pharmas, China will skip all of the above steps just as Africa skipped grid-based centralized electricity distribution and is jumping straight into off-grid decentralized solar power technologies.


4. Streamlining and Cheapening of Clinical Trials

It is extremely time consuming and expensive to get a drug approved. In 2000s, only 11 percent of drugs entering phase 1 clinical trials ended up being approved by FDA. Biotech startups that can make it to phase 3 usually end up selling themselves completely (or partially on a milestone basis) to big pharma companies simply because they can not afford the process. In other words, the final bottleneck for these startups in getting to the market on their own is clinical trials.

This problem is much more multi dimensional and thorny, but there is still hope:

• Time. Regulations are being more streamlined and thereby making the processes faster.

• Cost. Genomics and real world data are enabling better targeting (or - in the case of already approved drugs - retargeting) of patients and resulting in better responding cohorts and thereby driving costs down.

• Risk. As we get better at simulating human biology on hardware and software, parallelizability of experimentation will increase and thereby the number of unnecessary (sure to fail) experiments on human beings will decrease. In other words, just as in the software world, experiments will fail faster.

5. Democratization and Decentralization of Drug Development

As some of the largest companies in the world, big pharmas are intimidating, but from an evolutionary point of view, they are actually quite primitive. The existing fatness is not due to some incredible prowess or sustained success, it is entirely structural in the sense that the industry itself has not fully matured and modularized yet. (In fact, there is little hope that they can execute the necessary internal changes and evolve a contemporary data-driven approach to drug development. That is why they seek acquisitions, outside partnerships etc.)

If you split open a big pharma today, you will see a centralized quantitative brain (consisting of bioinformatics and IT departments) and a constellation of independent R&D centers around this brain. This is exactly what the whole pharma industry will look like in the future.

Once quantitative brain is split off and centralized, computation is democratized and commoditized, data is democratized, uniformized and centralized, and clinical trials is streamlined and cheaper, there will be no need for biotech startups to merge themselves into the resource-rich environments of big pharma companies. Drugs will be developed in collaboration with the brain and be co-owned. (Currently we have already started seeing partnerships between the brain and the big pharma. Such partnerships will democratize and become common place.)

Biology will start off in independent labs and stay independent, and the startups will not have to sell themselves to the big guys if they do not want to, just as in the software world.

Biology is way too complex to allow repeat successes. Best ideas will always come from outsiders. In this sense, pharma industry will look more like the B2C software world rather than the B2B software world. Stochastic and experimental.

We have already started to see more dispersed value creation in the industry:

“Until well into the 1990s, a single drug company, Merck, was more valuable than all biotech companies combined. It probably seemed as if biotech would never arrive—until it did. Of the 10 best-selling drugs in the US during 2017, seven (including the top seller, the arthritis drug Humira) are biotech drugs based on antibodies.”

- MIT Tech Review - Look How Far Precision Medicine Has Come

(I did not say anything about the manufacturing and distribution steps since the vast majority of these physical processes is already being outsourced by pharma companies. In other words, these aspects of the industry have already been modularized.)

cloud vs multi-cloud

In the cloud world,

• software wants to be free. Cloud providers are incentivized to offer all sorts of free goods to drive more data and compute usage, because that is basically how they make money. They are high volume / low margin infrastructural businesses.

• hardware wants to be virtualized. Just like sequencing centers aggregate, centralize and virtualize sequencers and offer sequencing as a service, cloud providers do the same for PCs and offer data storage and computing as a service. Users do not directly interact with the machines themselves.

In other words, cloud providers commoditize the stack above them via free-ization and the stack below them via virtualization, and thereby increase the percentage of the value they capture in the value chain.

Thinking pictorially we have the following situation:

Cloud World
Meat Strategy

Here, the stacks composed of small squares represent commoditized competitive markets with many players, and the monolithic stack represents a monopolistic market.

Thinking of the whole figure as a hamburger, we can say that the cloud world is “pro-meat”.

Notice that all stacks’ incentives are aligned horizontally, in the sense that they all want the entire industry to grow and the bottlenecks (wherever in the value chain they may arise) to be eliminated. (i.e. Think of industry growth as the horizontal expansion of all stacks) But stacks’ incentives are not necessarily aligned vertically, in the sense that one stack capturing more of the surplus generated by the entire value chain often implies another stack capturing less. (i.e. Dynamics among the stacks is often governed by zero-sum games rather than non-zero-sum games.) Hence each stack wants to democratize (i.e. commoditize) the neighboring stacks that it interacts with. (Read this older post for a deeper look at such stack dynamics.)

Now, a multi-cloud software strategy weakens the cloud layer (middle stack) by commoditizing cloud providers and thereby releases the tension on the hardware layer (bottom stack). Thinking pictorially we have the following situation:

Multi-Cloud World
Bread Strategy

This is essentially why IBM (after missing the cloud wave due to short-sightedness) ended up recently buying Red Hat for 34 billion USD:

This acquisition brings together the best-in-class hybrid cloud providers and will enable companies to securely move all business applications to the cloud. Companies today are already using multiple clouds. However, research shows that 80 percent of business workloads have yet to move to the cloud, held back by the proprietary nature of today’s cloud market. This prevents portability of data and applications across multiple clouds, data security in a multi-cloud environment and consistent cloud management.

IBM and Red Hat will be strongly positioned to address this issue and accelerate hybrid multi-cloud adoption. Together, they will help clients create cloud-native business applications faster, drive greater portability and security of data and applications across multiple public and private clouds, all with consistent cloud management. In doing so, they will draw on their shared leadership in key technologies, such as Linux, containers, Kubernetes, multi-cloud management, and cloud management and automation.

- IBM Newsroom - IBM to Acquire Red Hat, Completely Changing the Cloud Landscape

states vs processes

We think of all dynamical situations as consisting of a space of states and a set of laws codifying how these states are weaved across time, and refer to the actual manifestation of these laws as processes.

Of course, one can argue whether it is sensical to split the reality into states and processes but so far it has been very fruitful to do so.

1. Interchangeability

1.1. Simplicity as Interchangeability of States and Processes

In mathematics, structures (i.e. persisting states) tend to be exactly whatever are preserved by transformations (i.e. processes). That is why Category Theory works, why you can study processes in lieu of states without losing information. (Think of continuous maps vs topological spaces) State and process centric perspectives each have their own practical benefits, but they are completely interchangeable in the sense that both Set Theory (state centric perspective) and Category Theory (process centric perspective) can be taken as the foundation of all of mathematics.

Physics is similar to mathematics. Studying laws is basically the same thing as studying properties. Properties are whatever are preserved by laws and can also be seen as whatever give rise to laws. (Think of electric charge vs electrodynamics) This observation may sound deep, but (as with any deep observation) is actually tautologous since we can study only what does not change through time and only what does not change through time allows us to study time itself. (Study of time is equivalent to study of laws.)

Couple of side-notes:

• There are no intrinsic (as opposed to extrinsic) properties in physics since physics is an experimental subject and all experiments involve an interaction. (Even mass is an extrinsic property, manifesting itself only dynamically.) Now here is the question that gets to the heart of the above discussion: If there exists only extrinsic properties and nothing else, then what holds these properties? Nothing! This is basically the essence of Radical Ontic Structural Realism and exactly why states and processes are interchangeable in physics. There is no scaffolding.

• You probably heard about the vast efforts and resources being poured into the validation of certain conjectural particles. Gauge theory tells us that the search for new particles is basically the same thing as the search for new symmetries which are of course nothing but processes.

• Choi–Jamiołkowski isomorphism helps us translate between quantum states and quantum processes.

Long story short, at the foundational level, states and processes are two sides of the same coin.

1.2. Complexity as Non-Interchangeability of States and Processes

You understand that you are facing complexity exactly when you end up having to study the states themselves along with the processes. In other words, in complex subjects, the interchangeability of state and process centric perspectives start to no longer make any practical sense. (That is why stating a problem in the right manner matters a lot in complex subjects. Right statement is half the solution.)

For instance, in biology, bioinformatics studies states and computational biology studies processes. (Beware that the nomenclature in biology literature has not stabilized yet.) Similarly, in computer science, study of databases (i.e. states) and programs (i.e. processes) are completely different subjects. (You can view programs themselves as databases and study how to generate new programs out of programs. But then you are simply operating in one higher dimension. Philosophy does not change.)

There is actually a deep relation between biology and computer science (similar to the one between physics and mathematics) which was discussed in an older blog post.

2. Persistence

The search for signs of persistence can be seen as the fundamental goal of science. There are two extreme views in metaphysics on this subject:

• Heraclitus says that the only thing that persists is change. (i.e. Time is real, space is not.)

• Parmenides says that change is illusionary and that there is just one absolute static unity. (i.e. Space is real, time is not.)

The duality of these points of views were most eloquently pointed out by the physicist John Wheeler, who said "Explain time? Not without explaining existence. Explain existence? Not without explaining time".

Persistences are very important because they generate other persistencies. In other words, they are the building blocks of our reality. For instance, states in biology are complex simply because biology strives to resist change by building persistence upon persistence.

2.1. Invariances as State-Persistences

From a state perspective, the basic building blocks are invariances, namely whatever that do not change across processes.

Study of change involves an initial stage where we give names to substates. Then we observe how these substates change with respect to time. If a substate changes to the point where it no longer fits the definition of being A, we say that substate (i.e. object) A failed to survive. In this sense, study of survival is a subset of study of change. The only reason why they are not the same thing is because our definitions themselves are often imprecise. (From one moment to the next, we say that the river has survived although its constituents have changed etc.)

Of course, the ambiguity here is on purpose. Otherwise without any definiens, you do not have an academic field to speak of. In physics for instance, the definitions are extremely precise, and the study of survival and the study of change completely overlap. In a complex subject like biology, states are so rich that the definitions have to be ambiguous. (You can only simulate the biological states in a formal language, not state a particular biological state. Hence the reason why computer science is a better fit for biology than mathematics.)

2.2. Cycles as Process-Persistences

Processes become state-like when they enter into cyclic behavior. That is why recurrence is so prevalent in science, especially in biology.

As an anticipatory affair, biology prefers regularities and predictabilities. Cycles are very reliable in this sense: They can be built on top of each other, and harnessed to record information about the past and to carry information to the future. (Even behaviorally we exploit this fact: It is easier to construct new habits by attaching them to old habits.) Life, in its essence, is just a perpetuation of a network of interacting ecological and chemical cycles, all of which can be traced back to the grand astronomical cycles.

Prior studies have reported that 15% of expressed genes show a circadian expression pattern in association with a specific function. A series of experimental and computational studies of gene expression in various murine tissues has led us to a different conclusion. By applying a new analysis strategy and a number of alternative algorithms, we identify baseline oscillation in almost 100% of all genes. While the phase and amplitude of oscillation vary between different tissues, circadian oscillation remains a fundamental property of every gene. Reanalysis of previously published data also reveals a greater number of oscillating genes than was previously reported. This suggests that circadian oscillation is a universal property of all mammalian genes, although phase and amplitude of oscillation are tissue-specific and remain associated with a gene’s function. (Source)

A cyclic process traces out what is called an orbital which are like invariances that are smeared across time. An invariance is a substate preserved by a process, namely a portion of a state that is mapped identically to itself. An orbital too is mapped to itself by the cyclic process, but it is not identically done so. (Each orbital point moves forward in time to another orbital point and eventually ends up at its initial position.) Hence orbitals and process-persistency can be viewed respectively as generalizations of invariances and state-persistency.

3. Information

In practice, we do not have perfect knowledge of the states nor the processes. Since we can not move both feet at the same time, in our quest to understand nature, we assume that we have perfect knowledge of either the states or the processes.

• Assumption: Perfect knowledge of all the actual processes but imperfect knowledge of the state
  Goal: Dissect the state into explainable and unexplainable parts
  Expectation: State is expected to be partially unexplainable due to experimental constraints on measuring states.

• Assumption: Perfect knowledge of a state but no knowledge of the actual processes
  Goal: Find the actual (minimal) process that generated the state from the library of all possible processes.
  Expectation: State is expected to be completely explainable due to perfect knowledge about the state and the unbounded freedom in finding the generating process.

The reason why I highlighted expectations here is because it is quite interesting how our psychological stance against the unexplainable (which is almost always - in our typical dismissive tone - referred to as noise) differs in each case.

• In the presence of perfect knowledge about the processes, we interpret the noisy parts of states as absence of information.

• In the absence of perfect knowledge about the processes, we interpret the noisy parts of states as presence of information.

The flip side of the above statements is that, in our quest to understand nature, we use the word information in two opposite senses.

• Information is what is explainable.

• Information is what is inexplainable.
  
  
  

3.1 Information as the Explainable

In this case, noise is the ideal left-over product after everything else is explained away, and is considered normal and expected. (We even gave the name “normal” to the most commonly encountered noise distribution.)

This point of view is statistical and is best exemplified by the field of statistical mechanics where massive micro-degrees freedom can be safely ignored due to their random nature and canned into highly regular noise distributions.

3.2. Information as the Inexplainable

In this case, noise is the only thing that can not be compressed further or explained away. It is surprising and unnerving. In computer speak, one would say “It is not a bug, it is a feature.”

This point of view is algorithmic and is best exemplified by the field of algorithmic complexity which looks at the notion of complexity from a process centric perspective.

thoughts on aphorisms

Aphorism is the most concentrated form of wisdom. Always formulated in the present tense, with an eye on timelessness, it steers clear of ephemeral notions. Merely concerned with the unchanging generalities, it has absolutely no intention to change the world.

Unfortunately, despite its power, aphorism also happens to be the most ungrateful form of written expression, immediately assuming a life of its own. It fools its author by feigning authenticity during birth, and once born, it reveals its completely generic nature and longing for immediate anonymity. (Moreover, the closer the author hits the truth, the greater is the longing for anonymity.)

Contrast this with how science works. There is no such thing as a scientific aphorism because scientists are different creatures. They do not aim for universality, they aim for (and are less humble about) precision instead.

Also, aphorisms are great for destructing, not constructing. They are like stones that can be thrown at already existing systems of thought, not useful for building brand new systems from scratch.

Related posts: Thoughts on Abstraction, Deliberate Vagueness

spectrum of scalability

There are only two types of businesses that really matter, namely those that scale perfectly (totally inhuman) and those that do not scale at all (totally human).

For instance, both content production and information-heavy technology businesses scale perfectly. (It is costless to reproduce films and software.) Spotting and nurturing promising artists and technology entrepreneurs on the other hand do not scale at all.

Greatest returns in business come from highly scalable (and defensible) businesses, but the most vital ingredient in building such businesses is talent. In this sense, talent management is only one-step away from scalability, and that is exactly why its return profile is extremely nonlinear, mimicking that of scalable businesses.

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Media and technology worlds are structurally quite similar in the sense pointed above. But then how could media companies have been so slow and inept at crafting a legitimate response to the tech companies creeping into their domain?

Answer is very simple. Although media content scales perfectly as software does, it does not evolve after it is born. For instance, once a film is produced, it is done. Software on the other hand is born immature and goes through an evolutionary design process which slowly settles into an equilibrium. Media companies do not know how to guide this evolution. That is why they are prudently waiting for the equilibrium to emerge before making a move. (Think of Disney’s late response to Netflix.)

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Investing only in totally scalable and totally unscalable businesses is an example of the barbell strategy popularized by Nassim Nicholas Taleb.

Universities are embodiments of this strategy. They pool their resources into two buckets: facilitating research and teaching students. Research is a form of content production and teaching is a form of talent nurturing. (They are nurturing future researchers.)

forbes türkiye soru-cevap

Eylül ayı Forbes Türkiye dergisinde yayınlanan profil yazısı için kullanılan soru-cevaplar:

Forbes: Yatırım ve iş felsefenizi oluştururken sizi en çok etkileyen fikir / ders / öğüt neydi; bunlar kimden gelmişti ya da hangi olay neticesinde elde etmiştiniz?

Cevap: Babamdan karanlığa dalabilmeyi, korkmamayı öğrendim sanırım, ama aynı zamanda saf rasyonalitenin ne kadar yanlış bir şey olabileceğini gördüm. Annemden de kalbimin sesini dinlemeyi, yılmamayı öğrendim, ama aynı zamanda saf duygusallığın yanlış olduğunu gördüm. İyi ve sevilen bir işadamı olmak gerçekten çok şizofrenik bir ruh hali. Sürekli farklı şapkalar takmanız gerekiyor. Sanırım ailemizdeki uçlaşma beni bu anlamda yıllarca deneyimsel açıdan hazırladı.

Doktora hocamdan da çok şey öğrendim. Mesela cahilliğimi nasıl avantajıma kullanabileceğimi, problem çözerken en önemli adımın doğru soruyu yöneltmek olduğunu gördüm.

Forbes: Bugünün ekonomik / teknolojik ve konjonktürel şartlarında hayata yeni atılıyor olsaydınız işe nereden ve ne yaparak başlardınız?

Cevap: Akademik hayattan iş hayatına geçiş travmatik bir deneyim, özellikle de akademik hayatınız başarılı geçmişse. Girilmesi en zor okullara girmek için yıllarca çabalıyorsunuz, üniversiteden çıkınca neyi maksimize edeceğinizi şaşırıyorsunuz. İster istemez en zor girilen, en çok insanın yığıldığı, en konjonktürel işlere dalıyorsunuz. Bu kadar benzersiz insanın aynı hayaller peşinden koşmasındaki garipliği göremiyorsunuz. (Her insan benzersizdir, doğru eğitimle daha da benzersizleşir.) 

Bugün hayata yeni atılıyor olsaydım, önce bir nefes alırdım. Trendleri değil, içimdeki tutkuyu, beni farklı kılanı bulmaya çalışırdım. Fırsatlar hiç bir zaman bitmiyor. En büyük zaman kayıpları kendinizi iyi tanımamaktan kaynaklanıyor.

Forbes: Gelişmeleri de göz önünde bulundurduğunuzda önümüzdeki dönemde hangi coğrafyaların yatırım için veya pazar olarak daha cazip olduğunu düşünüyorsunuz?

Cevap: Yazılım dünyası coğrafya-bağımsız dinamiklere sahip. Ürettiklerinizi anında bütün dünya pazarıyla buluşturabiliyorsunuz. İlk müşterileriniz coğrafi açıdan en yakın olanlar arasından değil, çözdüğünüz problemden en muzdarip ve teknolojiye en yatkın olanlar arasından çıkıyor.

Ayrıca cazibenin biraz da erişilememezlikten kaynaklandığını unutmamak lazım. Mesela Çin çok cazip piyasa fakat orada iş yapabilmek müthiş zor. Farklı pazarların cazibeliğini kendi kapasitenizle birlikte tartmanız gerekiyor.

Forbes: Önümüzdeki dönemde Türkiye’deki iş insanları ve girişimciler için en büyük riskler neler?

Cevap: Bazen Türkiye'nin artık dibe vurduğunu, bundan sonrasınının kesinlikle daha iyi olacağını düşünüyorum, fakat bu konuda hep yanılıyorum. O yüzden fazla bir yorum yapamayacağım. Aslına bakarsanız Türkiye'nin en büyük riski artık yorum yapılamayacak bir ülke haline gelmesi. Geleceği tahmin etmek çok güç.

Forbes: Sizce gelecekte trend olacak sektörler hangileri? Genç girişimciler hangi alana yönelmeli?

Cevap: Trendleri tahmin etmek zor, fakat bazı meta-trendler çok net. Mesela teknoloji girişimleri gittikçe sofistikeleşiyor ve B2C'den B2B'ye kayıyor. Yapay zeka altyapısal anlamda her yere yayılıyor. Arada chat bot furyası gibi trendler gelip geçiyor ama yapay zekanın önemi sürekli artmaya devam ediyor. 

Genel olarak girişimciler trend peşinden değil, problem peşinden koşmalı. Sıfırdan bir şirket inşa ediyorsunuz, devlet bonosu alıp satmıyorsunuz. Bu çok zorlu ve uzun bir süreç. Yarı yolda altınızdan halının çekilmeyeceğinden, çözmeye çalıştığınız sorunun ekonomik öneminin azalmayacağından emin olmalısınız.

Forbes: Size göre servet sahibi olmanın en iyi/etkili/kısa/doğru/akıllıca yolu nedir?

Cevap: Olağanüstü başarıların bir sistematiği yok. Olsa da zaten, ilgili teknikler herkes tarafından hızlıca benimseniyor ve rekabet üstünlüklerini kaybediyor. 

İş dünyasının hem bilimsel hem de sanatsal bir yanı var. Bilimsel yanı ancak aptal hatalar yapmamanıza yardım edebiliyor. Esas sanatsal yanıyla diğerlerinden ayrışıp servet yaratabiliyorsunuz. Bu da ancak deneyimle, usta-çırak ilişkileriyle öğrenilebiliyor.

Bariz ama gene de altını çizmekte fayda var: Sadece çok çalışarak servet yapılamıyor. En önemli faktörler sizin kontrolünüz dışında gelişiyor. Doğru zamanda doğru yerde olmanız, dolayısıyla çok iyi fırsat kollayabilmeniz gerekiyor.

Forbes: Ne kadar karlı görünse de hangi faktör sizi bir yatırımdan vaz geçirir?

Cevap: Bir yatırım ne kadar erken aşama ise o kadar insan faktörü önem kazanıyor. Yaşam eğrisinin başındaki bir şirket adeta kurucularıyla özdeşleşiyor. Her şey gaz ve toz bulutuyken (bırakın karı, ortada ciro bile yokken) yatırımcı olarak kuruculara sonsuz güven duymanız gerekiyor. Dolayısıyla en ufak etik problem, karaktersizlik, yalan dolan beni direk vazgeçiriyor.

Forbes: İş hayatının başında olan girişimci/yatırımcılar için “…asla yapmayın” ve “...bunu mutlaka yapın” diyeceğiniz tavsiyeler neler olurdu?

Cevap: Girişimcilik herkese uygun bir meslek değil. Sıfırdan bir şirket kurup ayağa kaldırmak inanılmaz zor, riskli ve stresli bir süreç. Öncelikle sizi heyecanlandıran, ekonomik potansiyeli yüksek bir problem bulmanız ve bu problemin en önemli kısımlarına çözüm geliştirebilecek çekirdek bir ekip kurabilmeniz gerekiyor. Çoğu girişimci adayı daha bu aşamada takılıyor.

Yatırımcılara tavsiyem kendilerinin de girişim kurmayı denemeleri. Doğru girişimleri seçebilmek, girişimcilere doğru tavsiyeler verebilmek, tahminlerinizi ve beklentilerinizi gerçekçi seviyelerde tutabilmek için bu şart.

Forbes: Sizce önümüzdeki dönemde lider kavramı nasıl olacak? Geleceğin liderinin özellikleri neler olmalı?

Cevap: Bence her sektörün kendine has doğru liderlik özellikleri var ve bu özellikler zaman içerisinde değişmiyor. Zaman içerisinde değişen tek şey ön plana çıkan sektörler oluyor. Mesela şu sıralar tekrar teknoloji sektörü atağa geçti ve bu sektörün liderleri ve karakteristik özellikleri basında daha ön plana çıktı. 

Bu demek değil ki, diğer sektörlerde eğitimli, entellektüel veya vizyoner olmak çok önemli. Fakat gene de tüm liderlerin teknoloji dalgalarını iyi izleyip, ona göre pozisyon almaları ve erkenden adaptasyon süreçlerini başlatmaları gerekiyor.

meta product design ideas

Here are three meta principles for generating new product design ideas:

• Either focus on one domain and be an expert or cut across all domains and be a generalist. (e.g. Zappos vs Amazon, Seven Bridges vs Palantir)

• Either offer the barest essentials or address the need in the most comprehensive way possible. (e.g. WhatsApp vs Facebook, Simplenote vs Evernote)

• Launch a premium version of your product once it becomes widespread enough. (e.g. Youtube vs Youtube Red, Tinder vs Tinder Gold)

nested interests

As a great employee you are supposed to consider the company’s interests above your own’s, as a great citizen you are supposed to consider the country’s interests above your company’s, and so on. Our interests have a nested structure like that of a matryoshka doll.

What is interesting is that there is constant conflict among the dolls and the way we choose to resolve such conflicts depends mostly on one single parameter. During times of crisis we care about the largest entity relevant to the nature of the crisis, and during times of peace we care about ourselves as much as we can. (That is essentially why centralist macroeconomic policies work better during times of crises and decentralist ones work better during times of peace. Unfortunately communists and capitalists could never see their relative contextual strengths while fighting for absolute dominancy.)

Dysfunctional prioritization algorithms that do not exhibit this basic linearity result in dysfunctional societies that do not exhibit the right cohesiveness dynamics. That is why constant self-obsession is considered as a bad behavior. (Of course, what is bad is defined by the society, not by you. In other words, ethics is actually a sub-discipline of sociology. Only in academia it is considered as a sub-discipline of philosophy, as if one can reason a set of ethical values into existence by thinking alone in a room.)