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.

---

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.)

---

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.)

monopolistic tendencies

Achilles' heel of capitalism has always been its tendency to concentrate wealth into the hands of a few entities. As software is eating the world, this tendency is increasing.

As opposed to their traditional counterparts, software companies are born international and can scale at a much faster pace. Moreover, due to the winner-takes-all dynamics of information economics, it is not easy for new software companies to challenge the incumbents. The result has been a concentration of wealth unseen in history.


Problem 1: Lack of Regulation

Our regulatory frameworks are stuck in a bygone era in which monopoly was defined as charging unjustifiably high prices to consumers while technology giants do the opposite by either charging lower prices (Amazon) or not charging at all (Facebook).


Problem 2: Lack of VCs with Balls

Investors are afraid of funding ambitious startups that want to compete against the giants head on. Instead, they prefer pure blue ocean strategies that can be executed with relatively small budgets. There are much bigger battles waiting to be fought, but the masters are afraid of facing the monsters of their own creation.

In fact, the situation is even worse. Anything within the periphery of the giants scares the shit out of investors. (What if Amazon enters that space? What if Facebook incorporates that as a feature? Lesson: The theoretical real estate around a product can actually be more valuable than the product itself, if you are big enough scare off all the potential intruders.)

Of course, no ocean can stay red forever. Blood eventually diffuses and the ocean returns back to its original color. (i.e. New startups are born once a monopoly dies.) This is a fact all venture capitalists implicitly acknowledge but (due to their nature) they are too selfish to tackle the associated collective action problem.


Solution 1: Death by Gluttony

One solution is keep the status quo dynamics and accelerate them even further. Giants want to feed on new startups? Alright, let’s feed them even more startups! Let’s feed them so fast that they collapse under the weight of their own organizational complexities.

Two troubles with this approach:

• Complexity is like an overhead that requires an extra rent to upkeep. A big entity can use its monopoly rents to pay off its complexity rents, like a rich old man refusing to die.

• Unlike traditional companies, software giants are quiet capable of managing their own complexities. (After all, most organizational problems are of information theoretic nature.) Some have even managed to essentially transform themselves into healthy closed ecosystems, upholding the law of the survival of the fittest while also enforcing cooperation against the outside world.

Solution 2: Death Match

Let the giants attack each other’s turf. They have sufficient human and financial capital, and ego to deploy the sort of attacks that VCs do not have the balls to enable. (Microsoft attacking Amazon AWS with Azure, Facebook attacking Amazon with Instagram Business, Amazon attacking Google Adwords with Amazon Advertising etc.)

Two troubles with this approach:

• It is not difficult for a few giants to implicitly agree on not stepping on each other’s toes that much.

• It may take forever for them to launch these attacks since they have so many other opportunities.

Solution 3: Programmed Death

Any entity that becomes big enough sooner or later turns evil. (Remember Google’s “Don’t be evil!” motto?) This is a universal law that holds across many different domains.

Planned obsolescence puts an upper bound on how far an entity can expand in the time dimension. We need something similar in the spatial dimensions. Biology already solved this problem by programming death into cells in such a way that they die before they turn evil and become cancerous. Why not insert similar constraints into the legal foundations of companies?

Two troubles with this approach:

• Companies often honestly can not control their growth.

• Consumers left out due to growth controls may complain.