In industrial automation and enterprise IT infrastructure, value is shifting from physical hardware to the control plane and platform layer. Hardware isn’t dead; it has evolved into a strategic Trojan Horse to bring software, telemetry, data, and AI services into the customer’s facility. From Siemens transforming factories into an operating system with Xcelerator to HPE turning the VMware crisis into enterprise market share with Morpheus VM Essentials and GreenLake, the winners are not selling hardware or pure software; they are building platforms that bind customers into their ecosystem.

Thesis

In the 1990s, the main question was who built the best hardware. In the 2000s, the industry rushed into software, following the “software is eating the world” mantra. But just writing software wasn’t enough. Companies that tried to sell software as a standalone product, detached from the hardware, failed. The real winners were quietly building something else: platforms that bring hardware, software, data, AI, and services together under one roof.

Today, the game is entirely different: who manages the customer’s entire operation? Siemens, HPE, Schneider, ABB, Cisco, Dell, Emerson, and Rockwell no longer just sell hardware. They don’t just sell software either. They sell platforms. And once a company establishes this structure, it heavily dictates how the customer works.

This article breaks down why this shift is inevitable, why the “transition to software” narrative is incomplete, the difference between platform builders and those stuck in pure software, and the financial evidence behind it all.

Hardware Isn’t Dead, Its Role Has Changed

Let’s be clear: hardware is not dead. NVIDIA, ASML, and TSMC are among the world’s most valuable companies. A semiconductor shortage can halt the global supply chain. GPU clusters are the backbone of the AI revolution.

What changed is not the hardware itself, but its role in the business model. In the past, hardware was the final product. You sold it, the customer used it, and that was it. When the contract ended or the device aged, the customer could easily swap it out for a competitor’s hardware. This meant one-off revenue, unpredictable cash flow, and margin pressure.

Today, hardware is the physical carrier of the platform. Sensors, servers, PLCs, switches: these are entry points to bring the software and service layer into the customer’s facility. That’s where the real value lies.

Once installed, the platform built on that hardware starts running the customer’s daily operations. Design, production planning, disaster recovery, predictive maintenance, automated decisions: all of these happen at the platform layer. If a customer wants to remove the hardware, they basically have to rip out their own operational backbone.

The “Let’s Be a Software Company” Trend and the GE Lesson

This transition wasn’t straightforward. Most of the industry simply said, “we are moving to software.” Marc Andreessen’s 2011 “software is eating the world” article justified this push. Hardware companies started buying software firms one by one, rebranding themselves as “digital industrial companies.”

The clearest failure of this trend was GE Predix. In 2015, GE made a massive claim: it was going to be the world’s largest digital industrial company. It invested billions and built an industrial IoT platform called Predix. But they made a critical error: they positioned the software as a standalone product that could be sold independently of their hardware. Instead of tightly integrating with GE’s own equipment, Predix tried to be a generic cloud platform for everyone.

The result? A software library that didn’t match field realities and failed to embed into the customer’s actual operations. GE Digital was restructured and scaled down in late 2018, leading to billions of dollars in write-downs.

GE’s mistake wasn’t “moving to software.” Their mistake was thinking software was the final product. Looking at Siemens during the same period shows a completely different approach. Siemens bought UGS, Mentor Graphics, Mendix, and Altair starting in 2007. But they didn’t sell them as disconnected software products. Every acquisition was a building block for a unified system called Xcelerator. Design, simulation, low-code, computational intelligence: they combined all of these into a single platform architecture that sits on top of the hardware and directly embeds into the customer’s workflow.

The industry talk was a two-step plan: move to software first, figure out platforms later. But the winners focused on the platform from day one. That’s exactly what separates the winners from the losers: the losers thought software was the destination, but it was only halfway there.

The Trojan Horse Strategy: How Hardware Opens the Door

The software saturation in the B2B market makes this strategy necessary. With the rise of AI and LLMs, standard SaaS products can be cloned in weeks. If a software tool can be easily rebuilt, it’s hard for it to maintain a solid defensive moat.

Hardware lives in a different reality. R&D is expensive, manufacturing is complex, and the equipment has to run flawlessly in the field. But once it gets into the customer’s data center, factory floor, or power grid, it creates a footprint that rival software companies cannot easily access.

From the outside, it looks like a standard industrial sensor, a network router, or a server. But inside, it has telemetry capabilities, embedded software, and direct platform connections. These activate the moment the device integrates into the customer’s system.

This strategy works especially well in conservative industries. Sectors like defense, healthcare, energy, and manufacturing often resist pure software sales due to heavy bureaucracy and established habits. But they are much more willing to buy a familiar piece of hardware that solves a physical problem. Once the hardware is in, the embedded platform naturally takes over the operation. The physical asset is more than just a delivery method; it gives the manufacturer exclusive access to telemetry data, security certifications, and value-added services like predictive maintenance.

Siemens: Turning the Factory into an Operating System

Siemens’ transformation isn’t a coincidence, and it didn’t happen overnight. Traditionally known as a PLC and SCADA manufacturer, the company saw that physical components would eventually become commodities. They’ve been executing a strict acquisition and integration strategy for over twenty years: moving into PLM, CAD, and CAM by acquiring UGS in 2007; adding electronic design and simulation with Mentor Graphics in 2017; building a low-code layer with Mendix in 2018; and finally, the $10.6 billion acquisition agreement of Altair Engineering announced in late 2024.

The Altair Deal and the Xcelerator Portfolio

The Altair deal took this to the next level. When Altair’s high-performance computing and simulation capabilities merge with the Siemens Xcelerator platform, it goes beyond standard industrial software.

Customers don’t just design a product anymore; they use AI to run virtual stress tests for material fatigue, thermal dynamics, and electromagnetic interference. Here is the structure Siemens built: the PLC stays on the factory floor as physical hardware. On top of it sits a lifecycle platform that covers everything from product design to simulation, production optimization, and after-sales maintenance. Once a factory buys the machine, they can’t simply walk away from Xcelerator, the brain running it.

Siemens’ Growth and Profitability Metrics

Siemens closed FY 2025 with €78.9 billion in revenue and €10.4 billion in net income. In Q2 2026, orders jumped 18% to €24.1 billion. Their total order backlog hit a record €124 billion.

The Digital Industries software division alone brought in €1.6 billion, growing 14% year-over-year for the quarter. Organic annual recurring revenue (ARR) grew 11% to €5.5 billion.

Hardware sales are always vulnerable to US tariffs, delayed rail projects, and currency fluctuations. Despite this, Siemens maintains a solid 15.4% industrial operating profit margin. The reason is simple: the margins of the software and platform layer are independent of the hardware.

HPE: Building the Operating System for the Data Center

Just as Siemens integrates the factory, HPE integrates the data center in the exact same way. Originally known for selling servers, storage, and networking hardware, HPE realized early on that pure hardware margins were being squeezed out by cloud providers.

HPE’s strategic shift comes down to one core idea: the cloud isn’t a place, it’s an operating model. Put the hardware on the customer’s premises, but manage it as a service through the GreenLake platform. To build this, they acquired Zerto for disaster recovery, OpsRamp for AIOps, Morpheus Data for hybrid cloud orchestration, and Juniper Networks for the networking layer.

The VMware Crisis and the VM Essentials Opportunity

The clearest example of HPE’s platform strategy happened in 2026. When Broadcom bought VMware, shifted licensing models to core-based subscriptions, and caused prices to soar, it created a shockwave in the enterprise IT world. Customers faced a double cost problem: having to pay for the old license and the new infrastructure at the same time.

HPE immediately turned this into a market opportunity:

• Aggressive Pricing and HVM Architecture: With Morpheus VM Essentials, HPE delivered an enterprise KVM-based HPE HVM hypervisor layer that allows customers to manage their VMware ESXi clusters from a single console, offering up to 90% savings at around $600 per socket.
• Hardware and Storage Interlocking: HPE tightly integrated Alletra MP enterprise storage and advanced Virtual Switch networking directly into the control plane. They backed this up with aggressive incentives: a free year of licensing for customers buying AMD servers, dropping Zerto data migration licenses to $1, and giving three years of free distribution to 600 authorized resellers.

As HPE CEO Antonio Neri put it: once a customer adopts the hybrid control plane, the entire infrastructure falls under the GreenLake umbrella, regardless of the underlying virtualization technology. This strategy goes far beyond just selling servers.

HPE’s Financial Surge and Order Backlog

GreenLake’s ARR was $1.3 billion in 2023. By Q4 2025, it reached $3.2 billion with 63% organic growth. In Q2 2026, total revenue hit a record $10.7 billion, up 40%.

The Cloud and AI segment grew 32.7% to $5.5 billion, and Networking jumped 148% to $2.7 billion thanks to the Juniper integration. The AI order backlog now exceeds $6.3 billion. Non-GAAP earnings per share hit $0.79, beating Wall Street expectations.

Not Just a Two-Company Story

This isn’t an isolated trend for Siemens and HPE. Market leaders across various verticals are doing the exact same thing:

• Schneider Electric: Moved away from just selling traditional panels and switches to focus on its EcoStruxure platform. The company’s “Digital Flywheel” revenue grew 15% organically in 2025, hitting €25 billion: making up 62% of their total revenue. Their adjusted EBITA margin stands at 18.7%.
• ABB: Sold off its traditional power grids unit in 2020 to focus entirely on software, robotics, and electrification. By acquiring 3D machine vision startups like Sevensense, they turned standard hardware into “robotic agents.” In 2025, orders hit $36.8 billion, with a 19% operational EBITA margin.
• Rockwell Automation: Bought Plex Systems for $2.2 billion to layer cloud-based ERP and MES on top of its hardware. Total ARR grew by 8%.
• Emerson: Shifted from industrial sensors to process optimization software by buying the remaining 45% of AspenTech for $7.2 billion. The important detail here: Emerson paid 29 times AspenTech’s 2025 Adjusted EBITDA. When a hardware company pays that kind of multiple for a software asset, they are valuing it exactly like a SaaS company.
• Cisco: Bought Splunk to turn network traffic data into a security and observability platform. Today, 54% of its revenue comes from subscriptions, and total ARR is nearing $30 billion.
• Dell Technologies: With revenues exceeding $100 billion in FY2025, Dell isn’t just selling servers; through its Palantir partnership, it offers an on-premise AI operating system. Its AI server order backlog currently sits at $43 billion.

The Control Plane: The Modern Operating System

What these companies are building aren’t basic cloud dashboards or desktop apps. In technical terms, this is called a “control plane.” Traditional operating systems were built around deterministic processes: they relied heavily on human input and simple system calls. Modern data centers and smart factories are far too complex to be run by static rules.

This is where autonomous agent architectures come in: the pieces of the system (servers, CNC machines, network routers) gather data independently, assess the environment, and purpose-built AI agents talk to each other to keep things running.

For example: the digital twin feature in Siemens’ Xcelerator takes real-time data from a PLC on the assembly line, runs it through Altair simulations, and if it detects mechanical fatigue, it automatically tells the production planning agent to swap the part. This isn’t basic automation; it’s a probabilistic decision-making engine embedded right into the hardware.

This is the real advantage hardware companies have over pure cloud providers like AWS, Microsoft, and Google: they own the edge. A cloud provider can’t directly calibrate a sensor on a factory floor. Hardware makers, because they own the physical infrastructure where the data is born, can run these AI agents with the lowest possible latency.

Why Are Financial Markets Rewarding This?

The financial logic behind this shift relies on three main factors:

1. Recurring revenue: Instead of a one-time hardware sale, companies secure Annual Recurring Revenue (ARR) for as long as the customer uses the platform. HPE’s ARR hitting $3.2 billion or Cisco’s $30 billion subscription pool ensures steady cash flow, even during market downturns.
2. Securing future earnings: Through order backlogs and remaining performance obligations, these companies are guaranteeing their balance sheets years in advance. Siemens’ €124 billion backlog and Dell’s $43 billion backlog send a clear signal to investors: the future revenue is already locked in.
3. Valuation multiples: Markets typically give hardware companies low P/E multiples (around 10-15x). But software and service companies get much higher valuations. Companies that make more than half their money from recurring services (like Schneider at 62% or Cisco at 54%) are moving out of the hardware category and being valued more like SaaS companies. Emerson paying a 29x EBITDA multiple for AspenTech is proof of this value shift.

A Note on Vendor Lock-in

It’s important to make a distinction here. Yes, these platform strategies create high switching costs. But lock-in itself isn’t automatically a bad thing. Apple creates lock-in. SAP creates lock-in. Cisco creates lock-in.

The real issue isn’t the lock-in; it’s whether the value the customer gets justifies the dependency. If a factory managing its entire production lifecycle through Xcelerator is deriving real operational value from this platform, the switching cost isn’t a problem: it’s a choice. But squeezing the existing customer base with price pressure, like Broadcom did with VMware, is abusing the customer’s dependency. And the market reacts to that difference immediately. HPE’s VM Essentials is the exact product of this reaction.

Conclusion: They Are Selling Platforms

Hardware gets you in the door, but the platform controls the future. If you sell a physical machine, you are allocating a narrow capability to the customer. When the contract is up, they can replace it with a different machine. But if you install a platform like Xcelerator, GreenLake, or EcoStruxure on top of this hardware and dictate the design, management, orchestration, disaster recovery, and autonomous decisions through this platform; you become deeply entrenched in the customer’s working culture and operating model.

The winners of the next decade won’t be pure software companies floating in the cloud. They also won’t be pure hardware makers stuck in low-margin manufacturing. The winners will be platform companies that use hardware to get inside the customer’s operations, and then integrate them into an AI-driven ecosystem. This is where the real competition is happening.

References

Siemens Q2 FY 2026 Press Release and Earnings Call Transcript
Siemens–Altair Acquisition Announcement (SEC Filing, November 2024)
HPE Q2 FY 2026 Financial Results and Earnings Press Release
HPE Morpheus VM Essentials Product Documentation and QuickSpecs
CRN: HPE CEO Antonio Neri, Morpheus VM Essentials Interview (June 2026)
Schneider Electric FY 2025 Full Year Results Presentation
ABB Q4 2025 Press Release and Integrated Report 2025
Rockwell Automation FY 2025 Full Year Results
Emerson–AspenTech Full Acquisition Announcement and SEC Filing
Cisco FY 2025 Annual Report
Dell Technologies FY 2025/2026 Investor Presentations
Venture Atlanta: “Top Startup Industries to Watch in 2026” (Hardware-enabled software business models analysis)

Related Links

Siemens Xcelerator Industrial Platform Portfolio
HPE GreenLake Edge-to-Cloud Hybrid Control Plane
Broadcom VMware Licensing Changes and Enterprise Analysis

Last updated: October 2026 | Version: 1.0

User distribution charts frequently shared in technical newsletters and social media regarding artificial intelligence point to an enduring reality that predates modern software tools by centuries. From steam power and electrical grids to railway signaling and distributed cloud platforms, technological history can be analytically understood across four operational layers: those who live within systems, those who observe them, those who adapt them, and those who architect them. The fact that a tool becomes immensely popular or is used by billions does not mean that systemic control has been decentralized. In this analysis, we examine these four layers from consumption to architecture through the lens of systems engineering and institutional discipline.

The Illusion of the Showcase: Tools Change, Distribution Remains Constant

Every profound technological shift arrives accompanied by promises of universal democratization. When steam power spread, it was expected to eliminate the hierarchies of physical labor. When electrical grids enveloped cities, universal energy was heralded as an unconstrained equalizer. When personal computers and the internet emerged, the narrative insisted that centralized gatekeepers would dissolve, transforming every user into an autonomous producer.

The contemporary discourse surrounding artificial intelligence mirrors this historical loop: it suggests that anyone capable of typing instructions into a chat interface is instantly elevated to software engineer, designer, or systems architect.

Real field dynamics tell an entirely different story. As technologies mature and user interfaces become friction-free, consumption becomes trivial for the broader public; yet the underlying infrastructure complexity, capital expenditure, and architectural control concentrate into an increasingly narrow core. Universal accessibility does not equate to systemic mastery. As consumption diffuses downward, the asymmetry of control between system architects and end users does not evaporate: it deepens while becoming invisible.

To understand this dynamic without succumbing to the hype of transient technical cycles, one must analyze the four fundamental layers that define the relationship between human society and technological systems.

The Four Layers of Civilization: From Consumption to Architecture

Across every industrial epoch and discipline, the structural relationship between people and complex systems consistently stratifies into four distinct groups:

First Layer: Those Living Inside the System

The vast majority of society is born into existing systems, consumes their outputs, and conducts daily life entirely within their prescribed boundaries. For this layer, the system is not a deliberate human artifact governed by trade-offs; it is an ambient, natural condition analogous to gravity or weather.

Electricity is simply an outlet on the wall: the underlying frequency stability, high-voltage transformer substations, and interconnected grid topology are irrelevant details. Banking is an icon on a mobile phone: clearing houses, liquidity ratios, and regulatory frameworks remain unseen. Artificial intelligence is an omniscient dialogue box that provides convenient answers. Individuals in this layer are neither negligent nor passive; modern economic specialization naturally channels them into end-user roles. However, occupants of this tier possess neither the leverage to interrogate the system nor the authority to alter its trajectory.

Second Layer: Those Who Recognize What They Are Inside

One tier higher sits the cohort that recognizes the mechanics behind the interface. This group understands that the algorithm, visual workflow, or regulatory structure presented to them is not neutral. They perceive how social feeds manipulate attention spans, how enterprise cloud platforms establish vendor lock-in, and how search ranking metrics bias the discovery of knowledge.

Observers recognize system constraints with clarity; yet they do not possess the specialized technical scaffolding or operational mandate to modify the underlying machinery. This layer comprises sharp analysts, rigorous critics, and perceptive commentators. They diagnose systemic dysfunctions with precision, but they do not sit at the workbench where the foundations are forged.

Third Layer: Those Who Intervene and Operate

The third tier consists of practitioners who translate theory into working reality. Engineers, field specialists, system integrators, consultants, and operations leaders occupy this layer. They do not merely critique the system: they manipulate its controls with professional tooling.

The automation engineer optimizing PLC cycle times on a manufacturing floor, the enterprise consultant tailoring ERP data flows to corporate logistics, and the software developer connecting frontier model APIs to proprietary databases all belong to this group. Operators represent the operational backbone of industry: they guarantee resilience, throughput, and uptime. Yet they rarely draft the initial rulebook. Instead, they produce substantial commercial value by assembling and tuning libraries, standards, and hardware platforms created by the fourth tier.

Fourth Layer: Those Who Architect the System

At the apex of the structure resides a tiny minority: the architects who build the foundational systems from scratch. These individuals do not merely execute workflows within an established framework; they define the core protocols, physical constraints, and governance standards that dictate how everyone else builds.

In rail transport, they are not the operators driving locomotives: they are the CENELEC committees and signaling architects establishing vital interlocking standards. In the software ecosystem, they do not merely assemble web applications: they specify the TCP/IP stack, engineer the Linux kernel, or formulate distributed database consensus protocols. In artificial intelligence, they are not prompt authors: they are the systems architects designing distributed GPU interconnects, training harnesses, and deterministic execution engines. The fourth layer sets the architectural coordinates; the remaining layers operate within them.

The Cognitive and Practical Thresholds of Elevation

Progressing through these layers is neither automatic nor a passive consequence of tenure. Each transition demands a distinct cognitive shift and substantial deliberate friction:

From Consumption to Understanding

Consuming is comfortable because the user evaluates only the immediate output. Understanding demands genuine curiosity and cognitive investment. It requires opening the black box, peering beneath abstraction layers, and identifying the structural compromises upon which the system rests. Exploring the memory allocation model of a programming library rather than blindly calling its functions, or diagnosing packet latency rather than merely clicking a button, marks the initial step across this threshold.

From Understanding to Application

A wide chasm separates theoretical comprehension from field reliability. Transitioning from the second layer to the third requires absorbing the real-world friction of physical and commercial environments. Noisy cabling, edge-case user inputs, thermal drift, and stringent budgetary limits dismantle textbook assumptions. The practitioner is the professional who reconciles theoretical elegance with the unforgiving realities of field operations.

From Application to Architecture

The final elevation is the most demanding and the least populated. Building competent implementations with third-party tools is a respectable skill; however, engineering an underlying platform, standard, or harness upon which thousands of other developers can build with deterministic reliability requires an entirely different order of architectural maturity. At this stage, the governing question shifts from “Does this solve my immediate task?” to “Does this architecture maintain stability across edge cases?”, “How does it fail gracefully under load?”, and “Where are its formal safety boundaries established?”

Artificial Intelligence in Context: The Latest Exemplar, The Same Immutable Rule

The contemporary landscape of artificial intelligence provides an exact proof of this four-tiered hierarchy:

• The majority of the global population has either never interacted with artificial intelligence or remains entirely oblivious to its architecture.
• The overwhelming majority of active users interact with free chatbots as search substitutes or casual novelty tools (First Layer).
• A smaller cohort recognizes algorithmic hallucination risks, training data provenance, and environmental compute costs (Second Layer).
• Developers and enterprise analysts integrate model APIs, calibrate system prompts, and embed retrieval pipelines into commercial software (Third Layer).
• Meanwhile, the specialized engineers designing custom silicon architectures, training harness environments, model weights, and governed agent frameworks represent an elite fraction estimated in industry models at roughly four hundredths of a percent (Fourth Layer).

This distribution is neither a defect nor an injustice: it is the foundational operating model of human technological civilization. It is neither feasible nor desirable for every individual to design microprocessors or train foundational models. The critical danger lies in the cognitive dissonance of believing that typing prompts into a conversational UI elevates a user into a system architect. Utilizing an interface is legitimate; mistaking consumption for architectural mastery is strategic blindness.

Conclusion: Accurately Positioning One’s Role

The greatest drain on engineering capacity and organizational capital stems from ambiguity regarding where one stands within the systemic hierarchy.

For a technology enterprise, a manufacturing firm, or an independent engineer, the primary strategic question is never “Which newly released tool should we adopt this week?” The decisive inquiry is far more fundamental: “Where do we stand within this system?”

Will you remain a passive tenant inhabiting an ecosystem designed by others? Will you serve as an observer who analyzes systemic flaws without possessing the leverage to mend them? Will you operate as a disciplined practitioner who links tools to industrial reality? Or will you build the deterministic boundaries, protocols, and platforms that establish the rules within your domain?

Throughout history, interfaces, nomenclatures, and commercial buzzwords have rotated continuously. The fundamental hierarchy of systems, and the decisive weight of those who architect them, remains unchanged.

References

Arthur, W. B. (2009). The Nature of Technology: What It Is and How It Evolves. Free Press.
Simon, H. A. (1996). The Sciences of the Artificial (3rd ed.). MIT Press.
Norman, D. (2013). The Design of Everyday Things. Basic Books.
Gall, J. (1975). Systemantics: How Systems Work and Especially How They Fail. Quadrangle Books.

Related Links

Using the Methodology vs Being Used by It: Principles Over Dogma
Knowing What to Count: Engineering Precision and Commercial Strategy

The global competition in artificial intelligence is largely debated through the rhetoric of “developing your own super model.” However, developing a frontier model is a race that demands large capital, infrastructure, and state support. This article analyzes the technical reality of model distillation, the double standard embedded in major providers’ complaints, the fragile hope-driven economics of state-backed frontier races, and the rational positioning strategy for mid-scale economies like Türkiye. The core argument: sovereignty is not about building the largest model — it is about controlling the most critical data.

Key Takeaways

• Model distillation is mathematically inevitable; any system exposed via API can be approximately reproduced
• Developing frontier models is a race sustained by strategic state support, not free-market dynamics
• Major providers’ distillation complaints contradict the legal ambiguity surrounding their own training data sources
• The rational strategy for countries like Türkiye is not to become a frontier producer, but to build a balance of controlled dependency and local capacity

DECONSTRUCTING THE ILLUSION AND TECHNICAL REALITY

What Is Distillation and Why Does It Terrify the Giants?

Model distillation is a straightforward concept in the technical literature: training a smaller model (student) using the knowledge structure of a larger model (teacher). Formalized by Hinton et al. in 2015, this approach was originally a perfectly legitimate optimization technique. Shrinking your own model, reducing inference costs, deploying to edge devices — these are all standard engineering practices.

The concept has taken on an entirely different meaning over the past two years.

When Anthropic accused DeepSeek and several Chinese laboratories of conducting industrial-scale distillation attacks, a technical term was suddenly transformed into a geopolitical weapon. OpenAI’s Sam Altman had voiced similar complaints earlier. The shared argument from major providers is this: the outputs of models we trained at a cost of billions of dollars are being harvested via API to train competing models.

The technical reality both supports and undermines this complaint.

The supporting side: Paying for API access covers the model’s inference cost. It does not cover the right to reproduce the model’s internal structure, training data, or architecture. Major providers’ API terms of service make this distinction explicitly: “You may not use outputs to train your own models.” This is a contractual clause, and it is legally binding.

The undermining side: These same companies, when training their own models, have largely used open internet content — newspapers, blogs, academic papers, forums — without permission. robots.txt and similar bot-blocking files are a technical courtesy protocol, not a legal barrier. Compliance is optional. And many major providers have chosen not to comply. The result: while saying “don’t train models with our outputs,” they themselves have trained models with others’ content. This double standard seriously muddies the legal and ethical debate.

So if the issue is this gray, why is there so much anger?

Because the companies’ complaints are not a technical security report. Look at the word choices: “industrial-scale,” “fraudulent accounts,” “military, intelligence, surveillance,” “growing in intensity.” This is a strategic positioning text designed to create regulatory pressure, frame the issue geopolitically, and consolidate investor confidence. It would be a mistake to see this as a simple outburst of anger.

The underlying mathematical reality remains unchanged: if a model can be sufficiently queried via API, its input-output behavior can be approximately learned. This is a fundamental principle of learning theory. Is it legitimate? Not according to the contract. Can it be technically prevented? Practically, no.

What Is Lost and What Is Gained in Distillation?

The appeal of distillation lies in cost optimization: Compared to the catastrophic expenses of frontier models, you can produce a “functional” (though non-equivalent) model with minimal capital. A distilled model does not inherit the original’s training data or weights; it merely copies input-output behavior. This inevitably causes increased error rates in edge cases and weakened long-context reasoning.

However, in sheer commercial reality, when fueled by high-volume specific data, these losses vanish. Enterprise chatbots do not require general artificial intelligence; being purely “good enough” is exceptionally profitable.

This process mirrors the digital “knockoff product” economy: you mathematically approximate the original’s function cheaply, vaporizing the creator’s initial R&D edge and fatally suppressing their financial return on investment.

This is why major firms view distillation not only as a technical threat but as a fatal strategic blow. The reality that Chinese laboratories (such as DeepSeek) successfully used this exact methodology to match the intelligence benchmarks of large American models sent shockwaves vibrating entirely through Silicon Valley. When viewed from their own isolated perspective, predicting their extreme outrage is justified.

THE GEOPOLITICAL RACE AND MACRO REALITY

The formidable cost efficiency and capital pressure enabled by distillation reveals an underlying structural reality: Frontier model development operates far less as a commercial free-market dynamic and far more as a state-backed strategic infrastructure initiative.

The Frontier Race: A State Project Financed by Hope

Frontier model training cannot be logically explained by classical free-market dynamics or lean startup economics.

The digits essentially prove how completely surreal the entire landscape has become: Today, training a single top-tier frontier model demands clustered formations of roughly 100,000 Nvidia H100 chips, generating an estimated fixed hardware capital expenditure (CAPEX) in the range of $3–4 billion. When you compound this with hundreds of millions in brute-force energy bills and thousands of hyper-specialized elite researchers, this scale entirely shatters the boundaries of any standard “digital startup” balance sheet. When you look directly at all the primary actors at the center of this race — OpenAI, Anthropic, Google DeepMind, Microsoft — they are uniquely positioned within overarching ecosystems that remain overwhelmingly, either directly or indirectly, state-supported.

This structure resembles a space program, nuclear research, or semiconductor fabrication investment far more than a free-market game. In other words, it is a strategic infrastructure investment. So why are states channeling this much capital into this domain?

The official justifications are familiar: military superiority, cybersecurity, intelligence capacity, economic competition, geopolitical balance. All legitimate strategic concerns. But I believe that what actually legitimizes the scale of these investments is not concrete returns but rather hope. A grand expectation that artificial intelligence will be a transformative technology — changing the economy, defense, and scientific research. And this expectation has not yet been fully proven.

Since GPT-3’s release in 2020, the revenue model for continuously increasing compute investments is still unclear. A balance that makes these models commercially sustainable has not yet been established. States continue their support despite this because rival states are also investing — and the cost of leaving the race appears greater than the cost of staying in it. This presents itself more as a security dilemma than a rational calculation.

History has shown us similar cycles. The Cold War space race pushed both sides beyond their economic limits. Nuclear energy promised energy “so cheap it would eliminate the electricity meter” in the 1950s — that promise never materialized, but investments continued for decades. We are now observing the same pattern in artificial intelligence: grand promises, large investments, and returns that have yet to materialize.

This does not mean artificial intelligence is useless. Its concrete value in very specific domains — protein folding, image analysis, code generation — is indisputable. It is also clear that it is a technology that makes our lives easier, cheap for us personally but expensive for the world at large. But there is a wide gap between the narrative that “general AI will transform everything” and today’s reality. And this gap is being filled not by the magnitude of investments, but by the magnitude of hopes.

If at some point hope proves insufficient against mathematics — that is, when public cost exceeds perceived strategic benefit — the support mechanism breaks. And at that point, structures sustained by state support will collapse; only those generating real commercial value will remain.

At a likely equilibrium and saturation point, I consider it probable that 3-5 frontier model providers will sustain their existence with state-backed compute infrastructure, while the rest become integrators and fine-tuners. This resembles the current structure of the semiconductor industry.

Building Your Own Frontier Model: Prestige Project or Strategic Investment?

At this juncture, the question inevitably shifts to operational mid-scale nations like Türkiye: Should we organically develop a localized frontier model?

In a global arena dominated by the US-China axis demanding tens of thousands of top-tier GPUs and vast, uncompromised data pools, the immediate bottleneck is never raw intelligence—it is pure infrastructure scale. While isolated, organically trained local parameter models represent respectable technical milestones, attempting to genuinely deploy them commercially against large frontier systems is deeply unrealistic under current capital constraints. These forced initiatives frequently hollow out into simple academic prestige, political messaging, and public technology showcases.

The true competitive theater no longer resides inside mathematical parameter counts. It is isolated within secure data architecture, application layers, and verifiable sovereignty. The primary question is not “who builds the largest model?”, but rather: “with which model infrastructure and under whose governance do we execute our most critical strategic decisions?”

THE RATIONAL SOLUTION ARCHITECTURE

If developing frontier models has become a geopolitical competition accessible only to the largest economies, and foundational capabilities can already be accessed or approximated via open-weight models and distillation, where should mid-scale economies position themselves on this strategic landscape?

Positioning Strategy: The Hybrid Architecture and 3-Layer Execution Model

Here is exactly where the strategic operational trajectory gets seriously critical. Once it is broadly acknowledged that forcefully pushing directly into the frontier arms race is pointless for the nation-state, we must establish a master hybrid strategy effortlessly blending hardcore “State Security” reflexes in flawless parallel sequence with highly agile “Open Market” commercial realities—without ever letting them collide.

Open Models and the Leverage Effect of Local Data

The optimal rational pathway mirrors strategies seen in agile Eastern labs: adopting heavyweight open foundation networks (e.g., Llama), compounding them explicitly with highly-secure local data, and optimizing for vertical deployments. Tangible premium corporate value resides inside niche domain knowledge, never raw parameter counts.

The Application (Wrapper) Integration Economy

Within this ecosystem, the most capital-exposed participants are often the foundational hardware and infrastructure layers bearing billions in CAPEX. Over time, high profit margins and sustainable cash flows tend to concentrate in the application and integration layer. Unlike commoditized model infrastructure, integration solutions solve specific domain bottlenecks and capture proprietary operational data, allowing teams to retain lasting commercial value. Core foundational models face commoditization pressure, whereas domain-specific integration and execution retain enduring value.

Just as the centralized nation-state is completely strategically justified fearing losing highly-classified operational data into offshore global API pipelines, the agile private sector is equally justified explicitly focusing purely on architecting fast, highly profitable global vertical solutions leveraging those exact same APIs.

Hybrid Architecture: Three-Layer Structure

Sovereign privacy friction remains unquestionably legitimate, but fully air-gapping an entire digital nation stifles economic velocity. The structural remedy mandates three rigid operational tiers:

• Layer 1 (Critical Sovereign): Completely air-gapped, domestically localized GPU clusters tailored for military and critical infrastructure. Demands absolute control, not frontier scale.
• Layer 2 (Regulated Sector): Locked VPC or domestically-fenced cloud corridors deploying heavily-audited, quota-based APIs specifically for banking and national energy assets.
• Layer 3 (Commercial Hub): Complete global frontier API integration driving maximum speed and suppressed operating costs across all non-strategic private enterprise sectors.

Lasting Investment Targets Data and Audits, Not Models

As core APIs iteratively expire and frontier technologies decay into mainstream legacies, only three strategic fortresses remain permanently invaluable:

• Pristine Local Data: Structured institutional data-sharing pipelines hold infinitely more strategic permanence than any fleeting foundation model.
• Log Sovereignty: The paramount threat isn’t the model’s location; it is who monitors the prompt payloads and warehouses the behavioral logs. Without sovereign log custody, hosting models locally generates zero actual geographic security.
• Audit-Class Human Capital: Deploying 200–300 specialized researchers focused not on redundantly training foundational models from scratch, but on evaluating, auditing, fine-tuning, and securing open-weight architectures for reliable domestic deployment.

Non-Negotiables for AI Sovereignty

The minimum requirements a country must fulfill to claim sovereignty in AI can be listed as follows:

This package is comparable to a major infrastructure project. It is not more expensive than a highway tender. But its geopolitical value is far greater.

Conclusion

The distillation debate may look like a contract violation issue on the surface, but underneath lies a far larger power struggle: who will hold power in artificial intelligence?

Major providers frame distillation as a strategic threat — they are right, because their business models depend on it. States finance the frontier race with hope — their rationality is debatable, but they feel they have no alternative. Small and mid-scale countries launch prestige projects with the rhetoric of “we’ll build our own super model” — most will remain as technology showcases, and whether they can succeed is uncertain. In my view, a realistic strategy is none of these.

For Türkiye and every economy of comparable scale, the most reasonable objective is neither to become a frontier producer nor a passive consumer. This reasonable objective lies in building a foundational local capacity and maintaining control over the dependency that will inevitably sit on top of it.

Rather than redundantly duplicating the immense capital expenditures required for foundational training, tailoring open-weight models with proprietary local domain data and investing in audit mechanisms represents a far more rational allocation of national resources.

Of course, to manage this coherently and maximize its benefit, the capacity to read this map and chart one’s own course when necessary is essential. Sovereignty is not about building the largest model — it is about controlling the most critical data.

References

Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NIPS Deep Learning and Representation Learning Workshop.
Open Source & Enterprise API Agreements: Standard Terms of Service (ToS) constraints prohibiting the deployment of model outputs for competitive training.
Sectoral Reports: Infrastructure investment analyses and market positioning reports from major technology laboratories.

Related Links

AI Architecture: The Rule of Dancing with LLMs: Agentic Micromanagement

LLM Dynamics: Short Circuit in LLM Models: Why Does AI “Lie” to Us?

Economics & Reliability: The Cheap LLM Illusion: Why Cost Per Successful Run Matters More Than Token Price