Article

2.2 Symbiotic Bandwidths Interface

In IIQ, the capacity of the interface — the degree to which intelligence and understanding emerge — depends on two symbionts or even more symbionts. Not necessarily two; there may be three to four or multiple agents. These agents are not necessarily human.

Neither are they necessarily ecologically distributed nor strictly based on symbiosis. The interface can instead be controlled asymbiotically, where a prey species can outsmart a more intelligent predator species through informational asymmetries and advantages created by interface intelligence — such as the tiger moth jamming the bat's echolocation system in nature.

2.3 Interface Vectors in Computational Modeling for Emergent Intelligences

To measure Interface Intelligence, a dual-symbiont interface vectors model for computational simulation is proposed for quantitative analysis, interface intelligence scoring, and interpretation of interconnections. The model explains how intelligence emerges through interaction rather than existing solely within a single system. The performance of an interface — the capacity for shared understanding and emergent cognition — depends on two generalized symbiotic components:

Machine Symbiont – This generalization represents the system’s ability to provide high-bandwidth communication, adaptive feedback, and signal fidelity. Examples include AI algorithms, neural networks, and computational sensors capable of translating or amplifying information for biological partners.

Biological Symbiont – This generalization represents a human or other biological agent’s capacity to interpret, adapt, and optimize communication through the interface. This capacity can evolve naturally, be enhanced transhumanistically (e.g., neural or sensory augmentation), or be modified through posthumanist cultural and cognitive adaptation. In the future, this could also include cross-species participants, such as dolphins, orcas, or bats, communicating via AI-mediated channels or intelligent acoustic metamaterial augmentations.

The interaction between these two generalized symbionts defines communication fidelity, denoted by β. The notation may be further generalized depending on whether humans are included or excluded, whether other conscious biological species are considered, or whether the model permits biological agents more advanced than humans. The framework may even consider the hypothetical possibility, however extremely speculative, that xenobiological extraterrestrial life exists on other planets or potentially remains undetected within the observable universe, consistent with different interpretations and extensions of the Drake equation [30]–[37].

In a science podcast in which Nobel laureate physicist Saul Perlmutter participated, Jill Tarter, a leading researcher in the Search for Extraterrestrial Intelligence (SETI), [38] used an analogy to illustrate the limitations of concluding that extraterrestrial intelligence does not exist based on humanity’s limited search efforts. She compared the search to taking a single small glass of water from the Earth's oceans and looking for fish. The failure to find a fish in that single glass, she argued, would not justify the conclusion that no fish exist anywhere in the ocean. Similarly, the absence of detected extraterrestrial intelligence within the limited regions and dimensions explored by humanity cannot, by itself, constitute evidence that such intelligence does not exist elsewhere in the universe.

Extending beyond the fish analogy in the ocean, we may also need to consider the possibility of intelligence operating at scales far beyond the human level. Hypothetical extraterrestrial superintelligence may not necessarily exist within a physical or organizational scale comparable to that of individual humans. One possibility is a form of hierarchical or fractal intelligence, in which smaller intelligent units collectively constitute a higher-order cognitive system.

This concept can be loosely compared with biological organization: the human body consists of trillions of cells, while complex microbial communities coexist within and interact with the human organism. By analogy, it is conceivable — although currently unsupported by empirical evidence — that an individual organism could constitute a fundamental unit within a much larger, higher-order extraterrestrial intelligence.

Such a hypothesis expands the search for extraterrestrial intelligence beyond the assumption that intelligence must be embodied in human-scale organisms or technological civilizations. This possibility is motivated by the multiscale organization of biological systems, in which collective intelligence can emerge across hierarchical levels ranging from cells and tissues to whole organisms and larger collectives [39].

So far, this has been a top-to-bottom view. However, the above discussion illustrates how communication and fidelity bandwidth may become increasingly narrow between symbionts operating at impractical or radically different scales for interface intelligence.

To narrow down and reduce confusion for human readers here for this paper;), consider Fermi Paradox to stick with practical human scale buttom-to-top view for metasonic brain interface intelligence symbiosis explanation step by step between biological symbiont and AI-machine symbiont adapted within the range and scale of coherent subwavelengths of acoustic metamaterials like SFID meta-chiptags.\cite{saatchi2025} Then, the C_h is reindexed to c_b representing biological symbiont, the C_a is now c_m for machine symbiont, the β_h is replaced with β_b, β_a is replaced with β_m, and then we can define biological symbiont vector B_sv and machine symbiont vector M_sv, as following,

B_sv = c_b β_b ,     M_sv = c_m β_m (2)

Then Voalah! the interface intelligence, II, and fidelity function β can be scored with symmetric bilinear form which responds to reflections for interoperable feedback loops, and computationally modeled in the following,

II = B_sv^T · K · M_sv = c_bβ_m + c_mβ_b = f(C_h, C_a, β) (3)

Where K is a symmetric swap matrix, an exchange matrix, that multiplies of c_bβ_m scoring adaptive fidelity of biological cognitive capacity to connect interface bandwidth of machine symbiont. And c_mβ_b means additional cumulative augmented fidelity of machine cognitive capacity to connect to interface bandwidth of biological symbiont. We can also derive and extend the math with additional matrix operation to measure evolutionary directions with interface intelligence evolving vectors for symbionts in following,

M2B_pii = (e₁^TB_sv) (e₂^TM_sv)q = (c_bβ_m)e₁ = c_bβ_m 0 (4)

B2M_tii = (e₂^TM_sv) (e₁^TB_sv) = (c_mβ_b)e₂ = 0 c_mβ_b (5)

e₁ = 1 0 ,     e₂ = 0 1 ,     K = 0  1 1  0 (6)

Where the M2B_pii is the machine-to-bio interface intelligence evolving vector for posthumanism direction and B2M_tii is the cybernetic bio-to-machine interface intelligence evolving vector for transhumanism/transpecies direction. If we concatenate or form diagonal matrix for non-zero components of B_sv and M_sv, we can form interface intelligence matrix as following,

M_ii = diag(M2B_{pii_1}, B2M_{tii_2}) = c_bβ_m    0 0    c_mβ_b (7)

To translate for mathematical interpretations, M2B_pii is the first evolving eigen vector and B2M_tii is the the second evolving eigen vector of interface intelligence matrix M_ii, which shows two directions of metasonic evolution in realm of interhumanism. The cross terms c_bβ_m and c_mβ_b are eigen values λ₁, λ₂ in interhumanism showing mutual existence of both posthumanism, and transhumanism. Conventionally, transhumanism is a phase between human and posthumanism.

Interface intelligence evolution can be orthogonal, or exponentially bidirectional. Thus, the degree of synchronization and mutual adaptation determines the emergent intelligence augmentation, demonstrating that cognition is fundamentally relational and interface-dependent. This is can be imagined as cybernetic bio-machine interface intelligent (BMII) computational model which is inspired from nature’s biological interface intelligent (NBII). And we can now dig into numerical calculation to develop a novel computer simulation after an analogy from nature’s interface intelligence augmentation.

2.4 Natural Interface Intelligences

NBII example is indeed happening between Borneo bat and specific carnivorous pitcher plant that having symbiotic acoustic communication interface intelligent making them to have higher interface intelligence score more than other bats and carnivorous plants.

In brief, in the rainforests of Borneo, an extraordinary symbiotic relationship exists between a carnivorous pitcher plant (Nepenthes hemsleyana) and a bat species (Kerivoula hardwickii) [40] [41] [42]. Unlike most carnivorous plants that lure and digest insects, N. hemsleyana has evolved a deep resonant cavity and metasurface patterns that reflects the bat’s echolocation calls with exceptional clarity with coherent wavelength of bat’s ear [19]. This acoustic precision allows the bats to locate the plant easily and roost inside its pitcher, where they remain safe from predators. In return, the plant absorbs vital nutrients from the bats’ droppings — sustaining its growth in nutrient-poor soils. Neither the bat nor the plant possesses higher individual intelligence than their relatives. However, their shared metasonic interface — the acoustic echolocation between the bat’s sonar and the plant’s morphology — produces an emergent interface intelligence at the ecological level. This communication system forms what can be termed NBII: a cooperative cognitive function distributed across species through an adapted communication channel.

From the perspective of interface intelligence, this bat–plant system demonstrates the principle of dual symbionts. The bat acts as the more intelligent biological symbiont, refining its acoustic bandwidth and behavioral sensitivity to exploit the interface. The plant acts as the ecological symbiont, evolving morphological features that optimize signal reflection — effectively increasing the interface fidelity β for both species. Their survival advantage arises not from superior internal cognition ,but from the performance of their shared interface. This parallels the human–AI relationship: intelligence, adaptation, and creativity may increasingly emerge between systems, through continuous feedback and signal co-adaptation, rather than within isolated minds. This analogy beautifully mitigate to understand how interface fidelity, not internal complexity, determines interface intelligence and even emergent intelligences.

Certain symbiosis can form a natural interface intelligences; not always. For instance, more hypersensitive tiger moth prey species outsmarts more intelligent predatory bats by jamming scoring more numerically more than Bornean symbionts (Bat-plant) benefiting from an asymbiotic interface intelligence. In order to simulate and score interface intelligence, we first simulate it between two alive species rather than machines and then we extend to bio-cybernetic machines and artificial intelligence for BMII scoring. Table 1 is provided in appendix of Supporting Information to understand communication fidelity quantitatively.

2.5 Logics for Parameters to Quantify Interface Intelligence

It is possible to assign computational parameters to the symbiont vectors introduced in the dual-symbiont Interface Model. Table 2 presents the scoring of NBII, while Table 3 extends the model toward cybernetic BMII for the quantitative formulation of IIQ to approximate degrees of interconsciousness.

For the numerical assumptions, we provide explicit reasoning for each coefficient and bandwidth parameter: c_b, c_m, c_p, c_tm, β_p, β_b, β_m, and β_tm. The parameter c_b = 1 is assigned for human, since humans possess auditory and linguistic cognition, but lack natural echolocation cognitive process. In contrast, bats, dolphins, and orcas, which exhibit strong echolocation cognitive capability, are assigned c_b = 2.

For the Bornean pitcher plant (Nepenthes hemsleyana), we assign c_b = c_p = 0, as it lacks direct cognitive ability. However, it possesses structural acoustic bandwidth intelligence fidelity, thus β_b = β_p = 1. For bats, orcas, and dolphins, β_b = 1 is maintained because humans can partially perceive their sonic range, and deep-learning-based sound classifiers (machines) can be trained to interpret their echolocation data.

The parameter β_m varies from 0 to 3 across sonic frequency identifications (SFID) tags to advanced SFID Transponder AI systems [19], reflecting increasing levels of acoustic and sensory fidelity. Based on these parameters, numerical simulations can be executed to explore interconsciousness as a quantifiable and debatable phenomenon within Interface Intelligence scoring.

What is most fascinating, however, is not the numerical simulation itself, but the revelation that bandwidth plays a crucial role in determining the potential for both transhumanism and posthumanism to coexist within the framework of IIQ theory. Scientists often express concern over the “black box” nature of deep learning models—specifically regarding their internal pattern-recognition mechanisms rather than their inputs, outputs, or logical structure.

The IIQ theory suggests that this “black box” is, in essence, due to information asymmetry related to the communication fidelity and bandwidth capacity that define interface intelligence. Enhancing computational hardware such as GPUs increases c_m (the computational capacity of the machine symbiont), while β_m may remain constant. Conversely, brain–machine interface devices expand β_m, thereby increasing the system’s bandwidth and physical passive memory for information to make the interface more efficient. Or for another instance, Nested-learning algorithms increases the β_m.

If humans were to enhance their cognitive abilities biologically (e.g., through genetic modification) without connecting to machines, this would represent an increase in c_b while β_b remains unchanged. When humans learn additional languages with different letters such as Chinese, Korean, Spanish, Russian, French, or Persian, β_b increases because linguistic diversity expands human–machine communication bandwidth. Conversely, sensory impairments such as deafness or blindness decrease β_b. A small number of humans can naturally perceive infrasonic or ultrasonic frequencies, leading to an elevated β_b.

Similarly, if underdevelopment machines were engineered to perceive olfactory inputs capable of influencing human emotion, β_m would increase. This interplay of parameters captures both the beauty and the madness of IIQ theory: it is neither inherently superior nor inferior, but dynamic and distributed. The system can be posthuman when cognition is shared, transhuman when humans are directly involved, or transspecies when biological symbionts such as bats, dolphins, or orcas achieve higher interface intelligence scores than human–machine hybrids. In degenerate cases, interface intelligence may vanish entirely (II = 0), whereas in convergent cases—where transhumanism and transspecies interactions co-emerge—it may be exponentially augmented.

2.6 Quantifying Interconsciousness Model

In IIQ framework, interconsciousness refers to the dynamic overlap of two integrated information fields, Φ, that temporarily form a shared, hybrid Φ field corresponding to the quantity of collective consciousness involved. When such overlaps occur recursively across multiple systems, they generate a scalable, fractal conscious system.

The hierarchical structure of this model can be described as follows:

• Proto-consciousness: minimal integration (Φ > 0) and precursor awareness at the level of a single neuron or agent;
• Consciousness: strong internal integration and awareness within a single biological agent;
• Interconsciousness: bidirectional coupling between agents that produces a shared field of awareness (e.g., human–AI, bat–AI);
• Fractal consciousness: recursive hierarchies of interconscious links forming nested, self-similar structures of consciousness across all scales (from neuron to civilization).

In this hierarchical model, interconsciousness serves as the unit cell of fractal consciousness; fractal consciousness is the infinite recursion of interconsciousness. The topic of consciousness itself lies beyond the primary scope of this paper—these ideas arise as speculative reflections during the quantification of interface intelligences across species or transpecies intelligences.

Speculative example:

Consider a system composed of a bat (a biological consciousness) and an AI (a non-conscious algorithmic system) coupled bidirectionally. The bat’s echolocation signals and brain states influence the AI’s internal models, while the AI’s outputs in turn affect the bat’s actions or sensory inputs.

• The bat alone: conscious.
• The AI alone: not conscious.
• The bat+AI system:
  • If the AI merely provides data → not interconscious.
  • If the AI modulates the bat’s perception in real time and the bat’s feedback modifies the AI → a proto-conscious hybrid.
  • If both maintain continuous, state-dependent mutual influence → an interconscious transpecies system within a fractal consciousness.

we can define the hybrid function as follows,

Φ_interface = H(Φ_B, Φ_M, II) (8)

Φ_interface = Φ_B + Φ_M + w_BM (ΔS_B · ΔS_M) (9)

This expresses the interface integrated information (Φ_interface) between system M and B (e.g., AI machine and a biological bat), where:

• Φ_B = integrated information of the bat
• Φ_M = integrated information of the AI machine
• w_BM = coupling weight between them
• ΔS_B and ΔS_M = state changes in each system

Now, how can we quantify interconsciousness mathematically? The state changes in each system arise from bidirectional causality between the machine symbiont and the biological symbiont. In this case, we model the interaction as ΔS_B · ΔS_M = II, where II represents the degree of interface intelligence: either the fidelity of the machine symbiont increases, or the biological symbiont adapts more effectively.

The coupling weight, w_BM, ranges from 0 (no integration) to 1 (full integration of interface intelligence). If the AI is non-conscious, we assign Φ_M = 0, while Φ_B remains non-zero for the conscious bat. The interface-level integrated information, Φ_interface, will therefore become greater than Φ_B for any smallest non-zero value of w_BM provided that II is also non-zero.

When Φ_interface > max(Φ_M, Φ_B), the system transitions from individual consciousness to interconsciousness. When the bat falls asleep, its internal causal structure weakens—neural modules decouple during slow-wave phases.

Φ_B ↓ (10)

That is, the integrated information of the bat (Φ_B) drops toward minimal values. Furthermore,

ΔS_B ≈ 0 (11)

indicating that no meaningful new state changes occur within the bat’s neural network. Consequently, the cross-agent interaction term also diminishes:

w_BM (ΔS_B · ΔS_M) → 0 (12)

Thus, the total interface integrated information can be approximated as

Φ_interface ≈ Φ_M + Φ_Bsleep (13)

and since

Φ_Bsleep << Φ_Bawake (14)

the interface’s overall interconsciousness collapses toward the AI’s baseline integration—still functional, but biologically conscious dependent in phenomenological depth. If we replace Φ_B with a Bornea pitcher plant,

Φ_interface = Φ_P + Φ_M + w_PM (ΔS_P · ΔS_M) (15)

Accoding to IIT [44] [45] [46], consciousness corresponds to the amount of integrated information generated by a system’s internal causal structure. Plants do have information processing (e.g., chemical signaling, electrical potentials, networked roots, and light sensors). But these processes are slow, modular, and weakly integrated — not recurrent in the way neuronal loops are. Plants are likely proto-conscious (minimal internal integration), but not conscious in the sentient or subjective sense.

Let us consider an extreme case where Φ_P = 0. Suppose a non-conscious AI system such as SFID has learned and integrated behaviors that allow it to locate a pitcher plant and deliver animal feces to it whenever the plant’s reservoir becomes empty. In this scenario, the interface intelligence is defined as II = 1 (according to Table 3), and consequently, Φ_interface becomes non-zero. This implies that the system is proto-conscious—not yet conscious, but possessing a higher level of integrated information than the plant alone, for which Φ_P = 0.

If we assign a very small, non-zero value to Φ_P to represent minimal or proto-conscious integration, then at its most efficient coupling, Φ_interface could reach approximately Φ_P + w_PM, yet it would still remain within the proto-conscious regime since max(w_PM) = 1. Therefore, depending on the coupling strength and interaction dynamics, Φ_interface can be classified as non-conscious, proto-conscious, or interconscious—occupying a continuum between post-conscious and fractal-conscious states.

2.6.1 Advanced Interconsciousness Modeling

Now, let each symbiotic subsystem X ∈ {M, B, …} be described by a state vector s_X(t) ∈ ℝⁿ (or by an appropriate stochastic process). We cand define the state change over a time interval Δt as,

ΔS_X ≡ 𝒟(s_X(t + Δt), s_X(t)) (16)

where 𝒟(·,·) is a chosen norm or distance function (e.g., the Euclidean norm of state differences, the Kullback–Leibler divergence for probability distributions, or the magnitude of change within a feature space).

We can define an interaction information term integrated through interface intelligence:

II_MB ≡ 𝕀(ΔS_M, ΔS_B) (17)

where 𝕀 represents a measure of statistical or causal interaction—such as normalized mutual information at interface, or a causal metric defined as the product of directional transfer entropies:

𝕀_causal = TE_{M → B} · TE_{B → M} (18)

We introduce coupling weights w_MB, w_BM ∈ [0,1] that quantify the degree to which subsystem M influences B, and vice versa. The integrated information of the interface (a two-actor system) is then modeled as,

Φ_interface = Φ_M + Φ_B + α(w_MB w_BM) 𝕀(ΔS_M, ΔS_B) (19)

where Φ_X denotes the internal integrated information of subsystem X as a symbiont (as defined in IIT), and α(·) is a normalization function (e.g., α(u) = u or α(u) = u / (u + u₀)) chosen to maintain consistent and bounded units. Then, we can define the following criteria for interoperational conditions:

• Bidirectional causality: Both directional causal flows must be significant:

  TE_{M → B} > θ_TE,    TE_{B → M} > θ_TE (20)

• Interface amplification: The interface must exhibit greater integrated information than either individual subsystem:

  Φ_interface > max(Φ_M, Φ_B) + ε (21)

  where ε > 0 is a small safety margin.

• Classification by magnitude: we choose thresholds such that

  θ₀ < θ_proto < θ_conscious < θ_meta < θ_fractal (22)

  then classify the system as follows:

  • θ₀ < Φ ≤ θ_conscious: Proto-conscious,
  • θ_conscious < Φ ≤ θ_meta: Interconscious (shared or coupled awareness across agents). Meta-consciousness [45] [46] [44] is a second-order awareness.
  • Φ > θ_fractal: Fractal-conscious (recursive, nested integration across scales).

3. Results and Discussion

3.1 Debate in Realm of Interhumanism

At high levels of feedback fidelity — such as neural interfaces, adaptive multimodal AI, or acoustic cybernetic systems — the human and AI form a coupled cognitive organism. This creates what we may term interconscious possibility or interconsciousness: a distributed awareness across biological and artificial substrates similar to musical improvisation happens between a rhythm guitarist and a lead guitarist shredding in a music band. Rather than asking whether AI thinks, interconsciousness suggests asking how cognition migrates and stabilizes within the interface itself. Human may augment the interface intelligence system through posthumanism route indirectly or through transhumanism route directly and that’s why it is called interhumanism to compare in the following Table 4 with anthropocentrism, transhumanism, and posthumanism.

3.2 Dilemma for Intuitive Mechanism

IIQ theory reframes neural network “black-box” as an expanding shared epistemic membrane between species of intelligence. Interpretability does not depend on transparency within the model, but on the communicative fidelity between the human responses (e.g. bioacoustic muscle memory layer of guitarist's hand and neuromorphic brain interface layer of auditory cortex) and the metasonic AI system. When the fidelity bandwidth deepens — through linguistic, emotional, or neural synchronization — the combined system displays emergent creative and analytic capacity beyond either component alone. The bandwidth of fidelity of human is narrower and increasing slower between human and AI now causing information asymmetry to interpret the black-box phase for human to decode and understand the mechanism. And we see two AI agents communicate with each other better because their bandwidth fidelity is bigger and more efficient than human-machine. If human reach higher bandwidth fidelity than AI, then maybe the black-box became opened box by understanding more internal mechanism. That’s the imagination initiative which caused IIQ position itself in fidelity bandwidth in relationship with black-box problem.

The IIQ is ripe for debate, there are many plausible to hammer it, but it is developed through dummy simplification holistically that can be more straightforward to understand what interhumanism looks like and how we can measure interface intelligence level with applied math and scoring quantitatively thorough computational modeling of eigen values of posthumanism evolving vector and tranhumanism evolving vector.

3.3 Agents On Computer Siumations

The multi-modal AI agents were used to create the computer simulation environment for interface intelligence with IIQ computational model developed under human supervision. The first computer simulation was performed for interpretation and visualization of cross-terms comparison for interface intelligence scoring for interhumanism accounting the effect of both posthumanism (M2B evolving vector) and transhumanism/transspecies (B2M evolving vector) shown in Figure 1. The simulation result shows that there is possibilities that transpecies outsmarting tranhumans with B2M vector magnitude. The result also shows that tranhumans/transpecies (Bat-human-SFID) noticeably have more interface intelligence intensity and has both B2M and M2B vectors magnitude.

3.4 Extension of IIQ Theory for Transhumanism Behavioral Economics and Venture Simulations Application

IIQ is not just a theory or computer simulations, it can be extended as a quantitative metric intelligence model for financial analysis to measure interface intelligences for application in corporate leadership and managements in AI investments strategies. We can also measure a new metric for AI augmentations in AI companies to account for investments. We live in a behavioral economy and AI advancements are changing the behavior of every individual or entity; a 5% improvement on LLM model between rival AI companies like OpenAI and Anthropic can change the behavior of clients for avalanche of premium subscription cancellations and revenue decline; or even rival chinese AI companies like DeepSeek or Kimi reverse-engineered American LLM models and proposing higher scored models with open-weights or open-source as an innovative method for corporate espionage for competition in market and geopolitics polarization between China and America.

Plus, with considering extreme vision and opinions, transhumanism is the phase between human and posthumanism era. In contrast, the definition of interhumanism is era where human and non-human species are transpeices-symbionts living with machine symbionts forming higher resilient interface intelligence and hybrid civilization that human will reach and face in future. So human investors should invest more intelligently with computer simulations and financial forecast for AI and biorobotics companies they invest in.

Quantitative behavioral finance works on modeling phenomena when investors behave irrationally, but with IIQ computer simulations they will be nudged[47] to take accounts more metrics input to behave less irrational and mitigate risks; similar to nudge theory classic example for flies to reduce cost of cleaning in men's toilet. IIQ can be used as a computer model in venture simulations interacting with AI agents, real data, behavioral economics, and computational finance.

If IIQ is used as one of metrics among other financial ratios [48] [49] [50] and impact weighting accounting [51] metrics by professional investors, governments or corporate policy makers, then AI investments and stock market and AI augmentations investments strategies can be measured in multi-modal large language model booming token economy. For example, we went for the IIQ extended simulation to see how it can be used in computational behavioral finance for investments strategies and decision making rationally. To extend, Table 5 in supporting information for AI companies and their biological and AI machine cognitive abilities.

Benchmarking & Sensitivity Analysis

Dynamic IIQ computer simulations can provides more insight for sensitivity analysis for different interplaying parameters in interface intelligence. Video S1 and Video S2 are provided in GitHub repo to see the illuminating power of computer simulation to benchmark intensity of interface intelligence shown in Figure 2 for AI corporations as a dependent variable shown in Figure 3 to extend for financial metrics and valuations ranking in IIQ theory.

Token Valuation & Financial Ratio

Every AI company began working on large language models (LLM) foundations in a token economy. The token input and output has different cost for them and users. On one hand, the AI models with more inaccuracies will push users to explain with more tokens in prompt and more frequecies. This will increase the cost and reduce cost efficiency of the AI company. On the other hand, the more smarter model with less errors, reduce the cost and increase the efficiency. This means that the value of that AI company per less token and less energy use will be higher to reduce burning rate. In addition, the smarter the models are, the more interface intelligence intensity will contribute to that company. Therefore, we define a new token term as company's value per token (VPT).

The VPT is calculated based on token cost economy (TCE) parameters and corporate interface intelligence (CII) demonstrated statistically in Figure 4 for one venture simulation. The interface intelligence is also quantize based on c_b, β_b, and c_m β_m. For CII, the c_b is modeled with numbers of biological cognitive abilities such as number of employees of that AI company. β_b is modeled with biological modalities of communication fidelity such as abilities like human visions, olfactory sensing, human auditory sensors, linguistics, brain interface machine developments. Therefore for companies like Neurolink, the β_b = 3 is considered in simulation setup and for other human corporations without brain interface machines, the β_b = 2 is considered.

For corporate machines, β_m can be more modalities such as text, images, sounds, brain interface machine, transponders, infrared camera, piezoelectric, etc. Thus, for computer simulation, we considered β_m = 7 as a based line parameter setup. And finally for c_m we can assume number of GPU units available multiplied by LLM model score the AI company possess. The TCE for all AI companies is based on AI model efficiency, we assumed same as for AI companies for this simulation setup around $0.003. Table 5 in appendix is provided to summarize parameters for CII Simulation with taking accounts for the following equations.

c_{m_c} = corporate GPUs × cognitive score (24)

VPT($) = TCE ($) GPUs + c_{b_c} (25)

• c_{b_c} = number of corporate employees
• β_{b_c} = corporate biological fidelity
• β_{b_m} = corporate machine fidelity

Interpretations for Investor's Behavior

IIQ theory computer simulations interpretations are interesting in behavioral finance for investors. Venture simulations and benchmarking shows companies with potential of superintelligences and alien transpecies wil be the highest of chain in interhumanism realm. For example, interface intelligent corporations like Neurolink-xAI have strong tranhuman and posthuman vectors growing in future with higher valuations and more interface intelligence potentials.

The superintelligences companies are ranked and score high as well. Computer simulations results shows alien transpecies intelligences will slightly have superior power over superintelligences based on IIQ theory principles and interplaying parameters. For example, corporate cognitive capacity of machines, compared across different terms as illustrated in Figure 5, significantly influences the results; specifically, changes in GPU units – ranging from 30,000 to 400,000 – are shown in Figure 6 and Video S1 and demonstrably affect CII in Figure 7a,b and Video S2; VPT in Figure 7c,d and Video S3; TFR in Figure 7e,f; and animated changes to LLM scores, visualized in Videos S4, are visualized for investor analysis.

a Nudge for Algorithmic Trade

IIQ metrics such as II, VPT, TFR can also effect alogrithm and decision making like a nudge [52] [47] for human and bias of bots in stockmarket for investments. This might have positive and negative impact on investments, and bots can score AI companies vision based on intelligence, token and valuations in a portolio next to each other for automations or visually analyze for stock broker or corporate investors.

Future Works on Interface Intelligences

IIQ can be modeled with better logic and mathematical improvement in future to introduce more metrics and better visualizations in computer simulations. IIQ is also proving insight on which evolving vectors the artificial intelligence and neural biotechnology can augment human into posthumanism or transhumanism/transpecies direction find new communicaiton fidalities like SFID-tags and SFID AI systems 2.0 or 3.0 or brain interface machines.

Warning Possibilities

Based on IIQ computer simulations and its dynamic quantitative logics, transparencies and posthumanism superintelligences are warnining possibilites that is not remote to develop within next decades. Between transpecies and superintelligences, the metasonic transpecies will be way more dangerous to human if they become able to attached to superintelligence symbiotically. For example, on paper and computer simulations results, SFID AI system 2.0 and 3.0 will enable transpecies emergences dominant human intelligence. In the extreme cases, controlling interface intelligence will be become symbiotically decentralized, or asymbiotic, not hierarchical.