The Epistemic Gain is the measurable net benefit of deploying an artificial intelligence (AI) system in a real-world socio-technical environment, after accounting for three key determinants: how well the system fits the problem’s domain, the cognitive and ethical load placed on human oversight, and the systemic friction within the organization and context. Unlike conventional performance metrics, it is computable and designed to capture how much of the projected benefit survives the journey from lab metrics to operational reality.
Measuring Epistemic Gain requires a practice we already know well in industry: benchmarking. But here, benchmarking is not just a technical evaluation — it becomes a way of mapping how an AI’s potential interacts with the realities of deployment. The same principles that guide competitive analysis in manufacturing and services can be repurposed to reveal where AI’s projected gains hold, and where they evaporate.
Benchmarking
In the technical and managerial world I come from, benchmarking is the bedrock of strategy. It’s our ritual for confronting reality. The process seems straightforward: you know your products, process flows, and service architecture. Your internal map of the territory appears complete. The task is merely to acquire the same map from your competitors to find the deltas. But this is where the ritual begins to reveal its true, deeper purpose.
The first confrontation is not with the competitor, but with yourself. As you seek external data, you must ask: Is my map accurate? Does this diagram of how my factory works correspond to the territory of the factory floor? The rigorous spirit of benchmarking is not about comparing your story to their story; it’s about forcing your map to confront somebody’s territory.
We often proceed with radical uncertainty. We accept that having, say, 33.33% of the competitor’s information is enough to begin. This sparse data isn’t just an input; it’s a shock to our own model. The first discovery is rarely “they are 15% more efficient in development cost.” More often, “the way we measure efficiency is hiding critical flaws in the benchmarking process.”
This leads to the second, deeper confrontation. When you try to align your (now questioned) reality with the sparse external data, the comparison isn’t apples-to-apples. It’s apples-to-some-other-fruit-you-haven’t-accounted-for. This forces a harmonization. You must make choices and trade-offs, not in your strategy, but in your very perception of the world. What you thought was a simple act of measurement has become a cascade of critical decisions about what matters.
The benchmark isn’t the input to strategy; it’s often the beginning of the strategy itself.
From Benchmarking to Telemetry
In the context of AI — especially in software systems — the benchmarking stage is not the end. It is the calibration point for a second, equally vital discipline: telemetry. Once an AI system is deployed, the environment changes, data drifts, human oversight adapts, and organizational friction evolves. Without continuous telemetry — capturing operational metrics, oversight load, error profiles, and adoption patterns — your original benchmark decays in effectiveness.
Telemetry is the only way to maintain a valid measure of Epistemic Gain over time in safety-critical or regulated domains. It turns benchmarking from a one-off comparison into a living measurement system, where deltas are continuously tracked and interpreted against real-world performance as proposed in the Δ–η–ζ Model. Without this feedback loop, you risk managing your AI system on the basis of stale assumptions — the fastest way to watch projected gains dissolve into operational debt.
And Now, The AI
This brings me to AI. My industrialist’s prior is that once we fix the purpose and underlying technology (as product, solution, or even service), the logic of benchmarking should hold. We have a new kind of engine; we need to measure its performance, efficiency, and outputs against a desired state or a human baseline. Conceptually, what’s the difference with AI?
A deep-seated intuition, shared by many, suggests AI presents a principle of difference against human decision-making or even other complex technologies.
Why?
Why does it feel like we’re trying to benchmark a “new entity” if it’s just another machine?
I have tried to answer these questions, and as often happens in such cases, other questions have emerged.
The Philosophy of the Machines
On May 1, 2025 — a date significant in itself — I published Philosophy of the Machines – A Manifesto for Humans in the Age of Artificial Agents. I released the preprint on several platforms with the intention of opening a broad, multidisciplinary discussion: OSF, Authorea, and SSNR.
The work is structured into ten sections, and the tenth — conceived as a culmination — is devoted precisely to proposing a different perspective in AI benchmarking and introducing the AI (epistemic) Gain. This was no accident; it is the result of a deliberate journey.
An ambitious journey, based on the conviction that one cannot leap directly from the Philosophy of Information — and, for that matter, from the Philosophy of Engineering — to the Philosophy of Artificial Intelligence without first passing through the Philosophy of the Machines. I do not believe we are adequately prepared for such a leap. We have not yet established what I call a mindset oriented toward the nature of machines, and we cannot neglect — even for an instant — our own human nature.
Human nature must be prepared and trained for a reality in which humans and machines coexist. This is nothing new for the industry, but we still make considerable confusion when it comes to AI, almost neglecting the fundamental nature of the machine itself.
I firmly believe that AI benchmarking — or better yet, the assessment of Epistemic Gain — requires more than a purely technical-economic methodology. This step calls for a new way of thinking, a new philosophy in fact: the Philosophy of the Machines.
A Computable, Context-Aware Benchmark: The Δ–η–ζ Model
Section 10 of the Philosophy of the Machines Manifesto introduces a pragmatic alternative for actual measuring AI Gain:
Actual AI Gain%=100⋅[Δ⋅Foresight Efficiency−(η⋅Human Oversight Effort+ζ)]Where:
Δ (Delta) — Domain adaptability: how well the AI fits the problem’s structure.
η (Eta) — Oversight complexity: cognitive and ethical load on human evaluators.
ζ (Zeta) — Systemic friction: organizational readiness gaps, contextual ambiguity, cultural resistance.
This is not a leaderboard metric — it’s a system-level performance equation.
It forces an AI benchmark to account for:
Domain specificity — In safety-critical domains, such as rail control or clinical decision support, two models with similar raw accuracy can differ drastically in real-world value depending on the domain’s operational, regulatory, and human-oversight demands.
Human cognitive bottlenecks — How much verification effort humans must invest.
Real deployment friction — How organizational and cultural factors drag down adoption efficiency.
AI Epistemic Gain in the Real World
A robust benchmark must measure systems, not just models. But it requires a strong framework.
That means:
Integrating socio-technical context — Testing AI in workflows with real human oversight and operational constraints.
Quantifying epistemic load — Measuring time, attention, and expertise spent verifying AI outputs.
Penalizing systemic friction — Capturing ζ-type effects from misalignment, process gaps, and resistance to change.
Without this, we repeat the history of overpromised automation — ERP, RPA — where “20–30% gains” evaporated under the weight of integration complexity.
The Epistemic Trade-off
A few weeks after the Philosophy of the Machines, philosopher and professor Luciano Floridi released “A Conjecture on a Fundamental Trade-Off Between Certainty and Scope in Symbolic and Generative AI.”
Here, the focus shifts to the epistemic trade-off in AI, and to a conjecture that seeks to provide a formal representation of the epistemic capacity of AI systems through an inequality linking the “certainty-scope” of any given AI mechanism.
In response to Floridi’s work, I developed an analysis, Floridi’s Epistemic Trade-Off: An Analysis of the Operational Breakdown and Ontological Limits of “Certainty-Scope” in AI, that highlights several critical aspects and raises questions about both the formulation and the practical use of this conjecture. This preprint is also available on SSNR.
Below is the current version of my paper.
Epistemic Trade-Off: An Analysis of the Operational Breakdown and Ontological Limits of “Certainty–Scope” in AI
Abstract
Floridi’s conjecture offers a compelling intuition about the fundamental trade-off between certainty and scope in artificial intelligence (AI) systems. This exploration remains crucial, not merely as a philosophical exercise, but as a potential compass for guiding AI investments, particularly in safety-critical industrial domains where the level of attention will surely be higher in the future. However, while intellectually coherent, its formalization ultimately freezes this insight into a suspended epistemic truth, resisting operationalization within real-world systems.
This paper is a result of an analysis arguing that the conjecture's ambition to provide insights to engineering design and regulatory decision-making is constrained by two critical factors: first, its reliance on incomputable constructs—rendering it practically unactionable and unverifiable; second, its underlying ontological assumption of AI systems as self-contained epistemic entities—separating it from the intricate and dynamic socio-technical environments in which knowledge is co-constructed.
We conclude that this dual breakdown—an epistemic closure deficit and an embeddedness bypass—prevents the conjecture from transitioning into a computable and actionable framework suitable for informing the design, deployment, and governance of real-world AI hybrid systems. In response, we propose a contribution to the framing of Floridi’s epistemic challenge, addressing the inherent epistemic burdens of AI within complex human-centric domains.
Introduction
The epistemic limits of artificial intelligence (AI) are among the most urgent concerns in AI design, deployment, and governance.
As AI systems expand in scope and autonomy, the critical question arises: How do generality and certainty coexist, and what fundamental trade-offs constrain their co-presence?
In response to this challenge, philosophers such as Luciano Floridi have proposed formal conjectures, notably a fundamental trade-off between certainty and scope . Floridi’s reasoning is indeed grounded in solid empirical and philosophical foundations, offering a compelling intuition.
However, as this paper analyzed, his formulation introduces an unresolvable formalization that risks collapsing under its own abstraction, failing to account for the epistemic entanglement of AI within real-world socio-technical systems.
In this context, epistemic entanglement refers to the way in which the quality, relevance, and reliability (i.e., ‘certainty’)—as well as the actual breadth of applicability (i.e., ‘scope’)—of an AI system’s inputs and outputs are inherently shaped by the system’s interaction with its operational environment and human intervention.
Analyzing Floridi's Reasoning
Floridi’s conjecture unfolds through four steps:
Observation: AI systems face a tension between provable certainty and expressive generality.
Philosophical Framing: This tension reflects a deeper epistemological trade-off.
Formalization: He proposes an inequality (1−C(M)S(M)≥ k) involving certainty and scope, linking the latter to Kolmogorov complexity.
Lack of Operational Closure: The conjecture is not computable, lacks a generative model of epistemic processes, and cannot be practically applied to support system design or governance.
Kolmogorov complexity, formally defined as the length of the shortest binary program that outputs a given string, is a canonical example of an incomputable function—no general algorithm exists that can compute it for arbitrary inputs. While, for example, compression-based approximations are used in practice, they remain heuristic and context-dependent, suffering from epistemic opacity and implementation variability. This limitation poses a significant challenge for real-world engineering: any metric built upon Kolmogorov complexity inherits its non-verifiability, making it unsuitable for domains requiring auditable, testable, and bounded epistemic measures—especially in safety-critical systems. Moreover, such abstraction may inadvertently obscure compatibility with established inference frameworks—such as Bayesian-optimal reasoning and algorithmic probability—which already provide computable mechanisms for managing epistemic uncertainty and generalization. In this light, while Floridi’s formulation captures an intuitive trade-off, its reliance on incomputable constructs prevents it from being reconciled with methodological standards already employed in AI validation, scientific modeling, and decision science.
From Step 3, which inherits the ontological assumptions from the epistemological framing of Step 2, summarized as follows, the constraints begin to emerge:
Boundary assumption: AI systems are framed along a symbolic–generative dichotomy, with each class occupying distinct poles of the certainty–scope spectrum. This framing implicitly defines the conceptual extremes of the conjecture.
Ontological assumption: AI systems are treated as epistemically independent entities—abstract generators of certainty and scope—without accounting for their entanglement with the socio-technical environments in which they operate.
This prompts immediate key questions: In an era of increasingly hybrid and socio-technically embedded AI systems, can we still uphold such clear-cut boundaries? More importantly, can we assume that epistemic measures like “certainty” and “scope” are intrinsic to the machine, or are they co-constructed through domain variability, human oversight, and system context?
Moreover, the formulation introduced in it cannot be tested, applied, or even proven or disproven. As a result, Step 4 leaves the “observed problem” without providing any possible “measurable insight” linked to the problem’s underlying structure.
This raises a critical question: How can scientists, engineers, and managers rely on an uncomputable inequality as a meaningful measure of epistemic trade-offs in systems they must design, validate, and govern in real-world contexts?
Furthermore, considering the dynamic evolution of AI systems and their intrinsic entanglement with human and socio-technical environments, the “certainty–scope” correlation proposed by Floridi appears intrinsically static. This fixed structure represents a significant limitation, as the conjecture lacks both an explicit temporal dimension and a dynamic component. As a result, it fails to account for how the epistemic properties of AI systems evolve and are co-constructed over time within complex, adaptive contexts. The absence of a rationale for treating the trade-off as a static measure further weakens the conjecture’s ability to model or inform the design, implementation, and governance of real-world AI systems—processes that are, by nature, fluid, iterative, and time-dependent.
Reframing the AI Epistemic Challenge
What matters is not whether generality reduces certainty in principle, but whether we can model, measure, and manage that tension within deployed complex intelligent systems.
This requires moving beyond theoretical complexity toward frameworks that are:
As outlined in [, Section 10], such models must account for the limits imposed by:
Computational machinery and algorithms, characterized by their theoretical AI performance shaped through domain-specific constraints (ΔF).
Human oversight captured as epistemic influence (ηH).
System friction and contextual variability, introducing uncertainty and resistance (ζ).
This is the missing link in Floridi’s formulation: an epistemic model that can return to the source problem—a real-world design challenge—and offer not only philosophical clarity, but operational insight.
Floridi treats AI systems as mechanisms that bear epistemic tension between certainty and scope, but not as epistemic agents themselves. Their role is structural: they are modeled, constrained, and evaluated through epistemic lenses, but they do not participate in knowledge production as autonomous subjects. However, this structural framing leads to a key limitation: although Floridi acknowledges the broader complexity of AI deployment, the certainty–scope trade-off is formalized independently of the system’s operational context. As a result, it abstracts away the socio-technical entanglement that often defines the true epistemic profile of an AI system.
In contrast, the view proposed here shifts the focus from the machine alone to the full socio-technical system in which it operates. AI systems are epistemically relevant not because they possess knowledge, but because they contribute — under human oversight and domain constraints — to structured processes of knowledge generation, verification, and utilization. Epistemic properties such as certainty and scope, therefore, cannot be meaningfully assigned without reference to this broader system of co-construction.
Floridi’s theoretical model does not fail as an intuition but collapses as a usable construct — it initiates a valid philosophical query, yet withholds the operational framing required to resolve it within the systems where it seeks relevance (the problem’s underlying structure).
This limitation becomes particularly acute in the context of safety-critical applications. In such domains, any guiding metric — whether deterministic or heuristic — must be grounded in measurable constructs and domain-specific assumptions. Absent such grounding, it risks generating epistemically misaligned signals: metrics that appear principled, yet fail to map onto the scale, risk profile, or investment constraints of real-world environments.
In this light, heuristics are not objectionable per se — but to serve their function, they must rest on transparent hypotheses and traceable epistemic variables. Only then can they be meaningfully integrated into design validation workflows or early-stage Return on Investment (ROI) evaluations.
Conclusion
Floridi’s conjecture remains an insightful philosophical contribution; its fundamental reliance on an incomputable construct, though, severely restricts its practical utility. By attempting to close an epistemic reasoning loop through a fundamentally uncomputable measure, it leaves unresolved the operational translation of “certainty” and “scope” abstractions. This approach should move in the direction of operational closure at the system level—or in epistemic entanglement— which is essential in this specific context.
Incomputability is not inherently a flaw—until it is mistaken for an operational constraint. At that point, it ceases to function as a theoretical boundary and becomes an epistemic liability. From a systems engineering and managerial perspective, this raises a legitimate question: To what extent is it justifiable to allocate resources toward evaluating a heuristic constraint, and what actionable benefits—if any—can such an effort realistically provide within real-world systems?
This question for the AI cannot be answered abstractly; it must be addressed in terms of quality, time, and cost—the foundational axes of any engineering, product, and governance strategy.
Ultimately, the epistemic challenge it raises remains both urgent and open.
The analysis proposed here serves as an invitation to further interdisciplinary collaboration in developing usable epistemic frameworks for complex intelligent systems.
Acknowledgements
The author wishes to thank Antonella Migliardi for her invaluable assistance and curatorial support in the development of this paper. Her role as curator has been essential to both the conceptual refinement and the formal presentation of the argument.