Faruk Guney

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Faruk Guney

Faruk Guney

@farukguney

Founder and Engineer at Norck and Vareon.

California Frankfurt Katılım Kasım 2025
167 Takip Edilen39 Takipçiler
Faruk Guney
Faruk Guney@farukguney·
I don’t think it is simulation or parallel. But I do think we live in a universe with information being its smallest substrate. So reality is observer dependent. In this universe observer consumes compressed information to make a world model out of it. In this line of argument, AI will be the latest observer. It will compress information in a way that won’t be sensible to humans and it will find causal links that wasn’t possible before.
Faruk Guney@farukguney

I want to make a precise argument from the lens of my Theory of Compressive Realism. Theory of Compressive Realism starts with a simple systems fact. The default setting of the world is open and nonequilibrium. Anything that persists does so by maintaining constraints in the face of noise, drift, and perturbation. That maintenance is not free. It requires throughput and it pays irreversible costs in whatever thermodynamic ledger is the best audited description in the regime. In the same framework, an observer never touches the world directly. An observer only has access to a limited, noisy, bandwidth constrained stream. What we call stable objects and laws are the compressions that keep winning in that stream across regime changes. They are not metaphysical primitives. They are the most stable, consistent predictive codes we have earned. Now take the human mind. The human mind is not just a collection of behaviors that happen to reproduce. It is a maintained control system that must keep an organism viable while building a usable model of the world. It must do this under tight resource constraints. Limited energy. Limited bandwidth. Limited memory. Finite speed. Noisy sensors. A body that must be controlled continuously. A social environment where coordination matters. A lifetime that demands long horizon plans and a stable identity. Once you see the mind in that regime, something becomes obvious. Before you can talk about selection for clever tricks, you need a system that does not dissolve. A mind cannot be selected for if it cannot first exist as a stable regulator. So I am not denying evolution. I am questioning an explanatory style that treats natural selection as if it were the architect of cognition by itself. Selection is a filter. It retains what works. But what counts as workable is shaped by constraints that come before any specific adaptation story. Finite bandwidth forces compression. Noise forces robust estimation. Limited energy forces sparse signaling and efficient coding. Partial observability forces internal state and memory. Delays force prediction. Long horizon goals force hierarchy. Continuous bodily control forces tight feedback loops. Those are not optional design preferences. They are structural requirements for any agent that survives in an open world. This is why the mind looks engineered. Not because there was a designer, and not because selection designs in the way an engineer does, but because engineering is what you get when you study systems that must remain stable under constraints. The structure of cognition follows from the requirement to maintain a viable regime while acting under uncertainty. That also clarifies something that many evolutionary narratives underweight. A large fraction of cognition is not external problem solving. It is internal maintenance. Attention, affect, self regulation, memory management, identity, and social inference are not decorations on top of intelligence. They are the core machinery that keeps the system coherent enough to solve any external problem at all. And the easiest place to see this is in failure modes. If the mind were mainly a bag of independent adaptations, breakdowns would look like random parts failing. But many breakdowns are patterned in ways that track resource limits and stability loss. Stress, sleep deprivation, delirium, anesthesia, and depression often degrade coherence in repeatable ways. That is exactly what you expect from a constrained compression and control architecture pushed outside its viable operating regime. In my lens, those breakdowns are not marginal. They are diagnostic. They reveal what the system is built to do. There is one more piece that matters for humans…

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Faruk Guney
Faruk Guney@farukguney·
I understand the programming would be a better term to describe how to fix the issue, but I don’t think that’s the problem. The problem is thermodynamics, and it is not a unique human bug. Every non-equilibrium system uses feedback to hold a viable state while burning energy and exporting disorder, and feedback in this process is quite noisy and almost always delayed, sometimes by a large margin. Epigenetic programming is of our hacks to make feedback lower-level, faster, and with better signal-to-noise, but the physics still breaks it down over time. Aging is a control problem with hardware limits, not just a bug in the code.
Faruk Guney@farukguney

We are all trying to solve a control problem before the dissipation unravels us.

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David Sinclair
David Sinclair@davidasinclair·
Agree
Healthspan@healthspanmed

Aging follows a predictable pattern. That suggests it's not random damage—it's programmed. Dr. João Pedro de Magalhães @jpsenescence proposes an interesting perspective on the theoretical underpinning of aging: our DNA is the hardware, while the epigenome—the chemical tags that turn genes on or off—acts as software. Over time, this software may become maladaptive, driving aging rather than merely reacting to damage. Here's what you need to know. 👇 🔍 The Research In his paper "Ageing as a software design flaw," Dr. de Magalhães compares the epigenome to computer software executing genetic instructions. Early in life, this developmental program orchestrates growth from a single cell to a fully formed adult. But after reproductive prime, these same genetic scripts might start working against us—suggesting aging may be less about accumulated damage and more about developmental instructions gone awry. Key Concepts: • DNA as Hardware: Our genetic code remains mostly stable, like a computer's unchanging foundation. • Epigenome as Software: Chemical marks dynamically switch genes on and off, like software toggles. • Developmental Programs: These processes guide cell division and tissue formation but may later trigger deterioration. 📊 Core Findings 1️⃣ Epigenetic Clocks (Horvath Clock) • Dr. Steve Horvath's work reveals that roughly 400 genome sites can predict chronological age with remarkable accuracy. • This clock starts ticking almost from conception, suggesting aging isn't random—it follows an orderly pattern written into our developmental script. 2️⃣ Predictable, Not Random • Aging markers like grey hair or bone density loss unfold predictably, not chaotically. • Across species—from mice to humans—the pace of development correlates with lifespan. Mice live fast and die young because their growth software runs at breakneck speed. 3️⃣ Maladaptive Developmental Software • Presbyopia—the stiffening of the eye's lens—illustrates how growth processes beneficial in youth (lens expansion) become harmful in mid-to-later life. • Similar dynamics appear in other tissues through hormone changes and immune shifts past reproductive age. 📖 Why This Matters Traditional theories treat aging as a linear accumulation of damage. But if aging is part of a developmental program, wear and tear isn't the sole culprit. Instead, we're dealing with a quasi-programmed decline, where the very instructions ensuring reproductive success later drive degeneration. • Antagonistic Pleiotropy: Genes advantageous early in life (promoting growth, rapid cell division) can have detrimental effects later, once survival for reproduction is achieved. • Evolutionary Limitations: Natural selection strongly favors traits that help us pass on genes, but it's less concerned with what happens afterward—so flaws in the software persist. 🛠️ Interventions & Practical Applications If developmental software inadvertently fuels aging, slowing or resetting it could boost healthspan: 1️⃣ mTOR Inhibition (Rapamycin) • mTOR drives cell growth and metabolism—crucial for development early in life. Later, overactive mTOR can accelerate tissue damage. • Rapamycin, by dialing down mTOR, extends lifespan in yeast, worms, flies, and mice—and is being explored in humans. 2️⃣ GH/IGF-1 Modulation (Metformin, Caloric Restriction) • High GH/IGF-1 fosters rapid growth but can promote diseases in old age. • Metformin and calorie restriction both reduce IGF-1 levels, correlating with improved metabolic health and increased longevity in animal models. 3️⃣ Cellular Reprogramming (Yamanaka Factors) • Shinya Yamanaka's breakthrough showed that four transcription factors (Oct3/4, Sox2, Klf4, c-Myc) can revert adult cells to a stem-cell-like state, effectively resetting epigenetic age. • Full reprogramming poses cancer risks, but partial or cyclical approaches may offer a factory reset on aging without unchecked cell growth. 💡 Key Takeaway When we view aging as a continuation of developmental processes rather than random decay, a new frontier for intervention emerges. Rather than playing whack-a-mole with diseases as they appear, we can aim to modify genetic and epigenetic programs before pathology sets in. From targeting pathways like mTOR/GH/IGF-1 to exploring partial cellular reprogramming, the prospect of true anti-aging therapies may rest on hacking the same software that built us in the first place. 🔗 Read the Full Review Curious to dive deeper into the idea of aging as a developmental software flaw? Explore our analysis to learn how epigenetic clocks, cancer paradoxes, and species-wide comparisons all converge on one notion: aging might be a predictable, programmable process that we can slow—or even reset. gethealthspan.com/science/articl…

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Faruk Guney
Faruk Guney@farukguney·
@rohanpaul_ai There is going to be no point in building websites either. Human to web interface will change significantly.
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Rohan Paul
Rohan Paul@rohanpaul_ai·
Former Goldman Sachs executive Raoul Pal explains how AI is going to eat traditional software/SAAS. If your product is just software, agentic AI can reproduce it on demand, optimize it, and redeploy it to a better market. "Agentic AI means it’s like having Fiverr, a website of experts you can ask any question. It’ll go away and do the task.... Agentic AI will build, design the website, code it, register the domain name, figure out the branding, figure out the marketing, figure out the email list, figure out the whole thing. So then you and I are in competition. You’ve built this incredible new website. I just go to my AI and say, “Love Steven’s website. Can you just build it better. Boom. 3 minutes. How can we be entrepreneurs in software? Now there’s this theory going around that AI is going to eat software, and I kind of get it." ---- From 'The Diary Of A CEO and Raoul Pal The Journey Man' YT channel. (link in comment)
Rohan Paul@rohanpaul_ai

Chamath on how AI agents are making the "10x engineer" distinction disappear because the most efficient "code paths" are now obvious to everyone. Just as AI solved chess and removed the mystery of the best move, AI is doing the same for coding, making the process reductive and removing technical differentiation. "I'm going to say something controversial: I don't think developers anymore have good judgment. Developers get to the answer, or they don't get to the answer, and that's what agents have done. The 10x engineer used to have better judgment than the 1x engineer, but by making everybody a 10x engineer, you're taking judgment away. You're taking code paths that are now obvious and making them available to everybody. It's effectively like what happened in chess: an AI created a solver so everybody understood the most efficient path in every single spot to do the most EV-positive (expected value positive) thing. Coding is very similar in that way; you can reduce it and view it very reductively, so there is no differentiation in code." --- From @theallinpod YT channel (link in comment)

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Faruk Guney
Faruk Guney@farukguney·
software engineering is dead, and in fact deader than dead. Any company whose business is behind a silos of software code is no longer protected. there is literally no moat left. if your company is agent compliant is good, but if it is agent dependent, you'll be swallowed by the next upgrade. You can literally build Google Seach today, but not the real world high signal data, not the R&D, certainly not easily the infrastructure. Any time agents face a problem that requires software code, they'll write it, solve the problem and trash it. Coding will be like solving a math problem.
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Faruk Guney
Faruk Guney@farukguney·
if any of you want to build a business that will last longer than ai agent hypes, you must now come to understanding that moats have shifted. You can literally build Google today, but you still need data or services to consume from Google APIs, you still need infrastructure from Google Cloud. Moat is now data, infrastructure. If you run a B2B business the real moat is now your own data, R&D, and more importantly your relationship with customers, and suppliers.
Faruk Guney@farukguney

It is terrifying but in a different sense. Any business that can be replicated within hours is not business. It is telling us a very difficult to grasp but a real story. Anything built simply by swarm of agents is no longer a business. It is a fake business model that won't last not even months let alone years.

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Faruk Guney
Faruk Guney@farukguney·
It is terrifying but in a different sense. Any business that can be replicated within hours is not business. It is telling us a very difficult to grasp but a real story. Anything built simply by swarm of agents is no longer a business. It is a fake business model that won't last not even months let alone years.
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Faruk Guney
Faruk Guney@farukguney·
For the past 80 years, the coding skills was the bottleneck! It is gone! Literally gone! Incredible times! Anyone code now with natural language! Imagine what this new era makes scientists and engineers! It could literally make them 3x what they are! That means we will need not only new coders but for any scientists and engineer to stay relevant, they will have to push even further the limits. The limit is no longer human cognition and skills. It is willingness to act and solve challenging physical problems, recursively! Within this new era, acceleration is no longer a possibility but a necessity to stay relevant!
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Faruk Guney
Faruk Guney@farukguney·
If token based ai is going to be like electricity, people will find quite reliable ways to generate their own tokens. We need companies building and assembling wind turbines for AI. That might be the next step.
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Faruk Guney
Faruk Guney@farukguney·
The internet has scaled communication, but not judgment. AI has scaled certainty, but not truth.
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Faruk Guney
Faruk Guney@farukguney·
Nothing is considered income if you sit around and robots do the work on your behalf. One's income is measured by the value it creates in a supply and demand economic model. There is certainly no such thing as income, let alone universal and high in the economic model these people envision.
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Faruk Guney
Faruk Guney@farukguney·
We've built ARDA and now testing it heavily. ARDA is an autonomous research and discovery architecture, an agent-first causal discovery platform built on top of our proprietary, patent-pending Causal Dynamics Engine, that enables AI agents and human scientists to discover governing equations, causal structures, and scaling laws from raw observational data, with continuous learning from live streaming sources, full audit-trail governance, and out-of-distribution generalization across any domain or industry.
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Matthew Berman
Matthew Berman@MatthewBerman·
What are you building this weekend?
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Faruk Guney
Faruk Guney@farukguney·
Today I introduced my 10 year old daughter to Cursor and she immediately began building her first game using Codex and Gemini. Nothing will ever be the same again!
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