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The Erasure of Shared Arguments May Be Civilization’s Last Challenge

Indigotime — Sat, 05 Sep 2026 14:25:08 +0000

AI will either destroy them completely - or help us rebuild and strengthen them

Behind the loud headlines about living in a “post-truth” age lies a more complicated problem. People are not simply choosing between truth and lies. They are choosing between different kinds of arguments, different authorities, different definitions of rationality, and different standards of moral acceptability.

They are also deciding how much uncertainty information may contain before it ceases to count as evidence.

And, perhaps most importantly, they are deciding which contexts to build into their identities deeply enough that those contexts begin to shape their perception of the world. Arguments that fit the identity are accepted. Arguments that conflict with it are rejected - not necessarily because they are false, but because they are experienced as incompatible with the person’s understanding of reality, community, or self.

This is not entirely new. It did not begin with the internet, social media, or recommendation algorithms. It has always been part of human life.

What has changed is the scale, speed, and precision with which these processes can now operate.

We have never shared a single reality

The internet and algorithmic content recommendation did not create disagreement. They exposed and accelerated it.

Before social media, people still lived inside contextual bubbles. Those bubbles were often tied more closely to geography, class, religion, profession, political affiliation, or local culture. Newspapers, television channels, schools, workplaces, churches, and communities all helped form shared informational environments.

In a sense, the people themselves performed the work that algorithms now automate. They selected what counted as relevant, credible, respectable, or outrageous. They helped construct a common dataset - a shared informational context - from which members of a group learned how to interpret new events.

That context shaped the perception of arguments. It determined which sources seemed trustworthy, which uncertainties seemed acceptable, which assumptions required no explanation, and which conclusions felt self-evident.

The result was often an echo chamber. But it was usually a geographically or socially bounded echo chamber.

Digital platforms removed many of those boundaries. Algorithms began assembling enormous, constantly changing audiences around shared emotional reactions, political commitments, fears, and identities. They connected people who had never met but who interpreted the world through similar contextual frames.

The algorithm did not need to convince everyone of the same proposition. It only needed to discover which kind of argument was most likely to work for each group - and then distribute variations of that argument at scale.

AI turns persuasion into an adaptive system

The arrival of generative AI intensifies this dynamic.

AI can function as a machine for identifying which type of persuasion is most likely to succeed, restructuring an argument around that type, smoothing over contradictions, and giving weak or unsupported claims the appearance of strength.

It can imitate independent verification. It can produce several rhetorical versions of the same idea, each adapted to a different audience. It can make a claim sound scientific to one group, morally urgent to another, culturally authentic to a third, and personally liberating to a fourth.

The same underlying message can be scaled across millions of different informational bubbles, each with its own vocabulary, fears, values, and assumptions.

This is a much deeper danger than model hallucinations.

A hallucinating model may invent a person, event, quotation, or statistic that never existed. That is serious, but it is relatively easy to describe: the model produced a false fact.

The more dangerous possibility is an argument that is logically smooth, emotionally appropriate, culturally persuasive, and built on a distorted picture of reality.

Such an argument may contain no obvious error. Its deception may lie in the selection of premises, the omission of relevant context, the manipulation of uncertainty, the framing of alternatives, or the emotional assumptions embedded in its structure.

The result is not merely an “alternative fact”. It is an alternative way of making facts meaningful.

People do not always want the truth

Users may not want truth in the conventional sense.

They may want an explanation that reduces anxiety. They may want a formulation that helps them win an argument. They may want to justify a decision they have already made, confirm their membership in a group, protect a cherished identity, or restore the feeling that the world is understandable and under control.

Those desires are not necessarily irrational. Human beings need coherence, belonging, and agency. The problem begins when a system is optimized to satisfy those needs by manufacturing persuasive explanations rather than by helping people examine reality.

A user may also deliberately employ AI to mislead other people. They may generate different arguments for different audiences, specifically designed to trigger the beliefs and emotions of each one.

This is not a science-fiction scenario. Political campaigning has already demonstrated the power of targeted messages, audience segmentation, psychological profiling, and emotionally calibrated persuasion. AI makes such operations cheaper, faster, more adaptive, and accessible to far more actors.

The crucial point is that AI does not make people stupid or irrational.

It makes irrational and knowingly false arguments rhetorically more competitive.

The problem existed before AI, especially in organized propaganda and political manipulation. But AI makes the tools of manipulation widely available and capable of operating at mass scale.

The illusion of a shared argumentative foundation

This brings us to the deeper civilizational challenge.

The widespread use of synthetic arguments exposes a problem that existed long before social media: the concept of a “shared argument” was never as shared as we assumed.

There is no universally accepted agreement about what counts as a claim, what counts as evidence, how much uncertainty evidence may contain, where a fact ends and interpretation begins, or how much context can be treated as part of an individual’s identity before it alters their perception of the argument itself.

There may have been an illusion that such an agreement existed.

AI is now destroying that illusion.

We are discovering that people do not merely disagree about conclusions. They often disagree about the structure of reasoning that makes a conclusion legitimate. They disagree about which premises may be taken for granted, which sources deserve trust, what kind of evidence is relevant, and whether moral or emotional intuitions should count as legitimate reasons.

In other words, the conflict is not only about what people believe. It is about what they believe an argument is.

This distinction matters. If two people disagree about a fact, they may still share a method for resolving the disagreement. They can compare sources, examine evidence, identify errors, and update their beliefs.

But if they disagree about what qualifies as evidence in the first place, the disagreement becomes much harder to resolve. They are no longer arguing inside a common framework. They are arguing over the framework itself.

From isolated errors to cascades of catastrophe

Throughout history, many disastrous decisions have resulted from insufficient information, distorted reasoning, or the use of persuasive arguments to conceal uncertainty, flawed assumptions, and conflicting interests.

Such arguments can survive because they provide something people need. They simplify complex situations. They offer a clear enemy, a decisive solution, a morally satisfying explanation, or a promise of control.

The danger is not that every AI-generated argument will produce an immediate catastrophe.

The danger is that synthetic arguments may become normal infrastructure for decision-making. They may enter politics, business, medicine, education, diplomacy, law, and everyday social life. People may increasingly rely on arguments optimized for persuasion rather than for accuracy.

The consequences may initially appear harmless. A misleading explanation may win an online dispute. A distorted narrative may help a campaign gain support. A simplified justification may enable an organization to avoid responsibility.

But when such decisions accumulate across institutions, the effects can become systemic.

Some consequences will be visible quickly. Others may remain hidden until the conditions they create interact with one another. A policy based on a persuasive falsehood may alter incentives. Those incentives may produce new data that appears to confirm the original belief. Later decisions may then be made on the basis of both the original error and the artificial evidence it helped generate.

This is how isolated distortions can become cascades.

AI could also help rebuild common reasoning

Yet the same technology that sharpens the problem may also help solve it.

Several research directions are already relevant, including argument mining, computational argumentation, and persuasion-technique detection. But a meaningful solution would require more than a model that labels content as “true” or “false”.

The first component should be an AI system designed to extract the structure of arguments from text and hypertext.

Its task should be content-agnostic. The subject matter, political position, cultural context, or conclusion of an argument should not determine whether the system performs its core function. Its job would be to identify claims, premises, conclusions, counterarguments, assumptions, evidence, inferences, qualifications, and relationships between them.

The extracted structure could then be added to the original text as a dedicated layer of machine-readable annotation.

A demonstration version might use a language such as Argdown. For broader and more sophisticated applications, a framework closer to the Argument Interchange Format could provide a stronger foundation.

The model should also be genuinely open - not merely downloadable or available through an API. Every stage of its creation should be auditable: the training data, annotation guidelines, evaluation procedures, known limitations, and changes between versions.

This distinction is essential. A model can be publicly accessible while remaining fundamentally opaque. If it is meant to help people evaluate arguments, its own construction must be open to evaluation.

The system should also be lightweight enough to operate on ordinary consumer devices. Argument analysis should not become a privilege available only to people with expensive hardware, premium subscriptions, or access to centralized platforms.

The second component should be deterministic software that visualizes the extracted argument structure and checks it for logical errors, unsupported transitions, contradictions, missing premises, and manipulative techniques.

It could identify patterns that make weak, irrational, or false arguments appear stronger than they are:

false dilemmas;
circular reasoning;
appeals to popularity or authority;
equivocation;
cherry-picking;
unjustified generalizations;
emotional substitution for evidence;
strategic ambiguity;
moving the goalposts;
selective framing;
attacks on the person rather than the claim;
and attempts to conceal uncertainty behind confident language.

This system should not pretend to know whether an entire article is “true”. That would simply replace one opaque authority with another.

Instead, it should show how the article’s reasoning is constructed.

It might indicate that one claim follows from another through a valid inference. It might show that a conclusion depends on an unstated assumption. It might highlight that a piece of evidence is relevant but insufficient. It might mark a rhetorical technique commonly used to create an impression of certainty where certainty is absent.

The tool would not say: “This article is true; all facts have been verified”.

Nor would it simply say: “This article is false”.

It would say something closer to: “Here is the structure of the argument. Here is where the evidence supports the claim. Here is where the reasoning depends on an assumption. Here is a recurring persuasive technique that may be misleading in this context”.

The final decision would remain with the reader.

That is not a weakness. It is the point.

The goal is not automated trust

A system designed to restore shared reasoning should not tell people what to believe. It should make the process of deciding easier to inspect.

The objective is not to create an automated arbiter of truth. That would be both unrealistic and dangerous. Any such arbiter would itself become a target for political capture, institutional bias, commercial pressure, or ideological control.

The objective is to create a common visual and analytical language for discussing arguments.

People may continue to disagree about premises, values, priorities, and conclusions. But a healthy civilization requires the ability to distinguish disagreement from manipulation, uncertainty from deception, and an unpopular argument from a structurally dishonest one.

At present, AI is rapidly improving the ability to produce arguments that feel individually tailored, emotionally satisfying, and rhetorically credible. If we do nothing, this may further erode the already fragile foundations of common reasoning.

But AI can also help us expose those foundations.

It can show which premises an argument relies on, which connections are justified, which uncertainties have been hidden, and which persuasive techniques are being used. It can make the architecture of reasoning visible without claiming ownership of the final judgment.

The challenge is therefore not simply to make AI more accurate.

It is to make arguments more inspectable.

If shared arguments disappear entirely, societies may lose the ability to resolve disagreements without resorting to power, identity, coercion, or violence. That is why the problem may be civilization’s last challenge - not because it guarantees a final catastrophe, but because it concerns the very mechanism through which collective decisions are made.

AI may complete the destruction of our common argumentative world.

Or it may help us rebuild one.

The outcome will depend on whether we use it primarily to win arguments - or to understand how arguments work.

Sustainable Resource Management Protocol for Multi-Agent Systems (AI Safety Specification)

Indigotime — Sun, 30 Aug 2026 22:22:55 +0000

0. Introduction

Long before the emergence of autonomous machines, humanity began imagining rules that would prevent intelligent mechanisms from turning their own power against their creators. The most famous attempt was Isaac Asimov's Three Laws of Robotics: a robot may not harm a human being, must obey human orders, and must protect its own existence, so long as doing so does not conflict with the first two laws.

Asimov later developed the idea of a Zeroth Law, placing the well-being of humanity as a whole above that of any individual:

A robot may not injure humanity, or, through inaction, allow humanity to come to harm.

These laws were a work of fiction, but their significance has long extended beyond science fiction. They offered the first intuitive formulation of a question that must now be addressed not for imaginary robots, but for real autonomous systems: what constraints should bind an agent that controls resources, makes decisions, and interacts with other agents without continuous human supervision?

The laws of robotics, however, remain moral declarations rather than engineering specifications. What exactly constitutes harm? Whose interests are represented by the terms human or humanity? How should an agent act when one subject's order conflicts with another subject's safety? Who determines whether a threat is real and whether a response is proportionate? How can compliance with a rule be verified in a system that changes its own state, operates with incomplete information, and may act faster than a human can understand what is happening?

The paradox is that human beings have still been unable to establish universal rules even for themselves. We continue to argue about the boundaries between freedom and coercion, the fair distribution of scarce goods, acceptable levels of inequality, and the circumstances in which an emergency justifies the temporary restriction of rights. We have no universal agreement on what justice is, yet systems already exist that will have to allocate computing capacity, access to data, energy, bandwidth, and the authority to modify the shared state of infrastructure.

The formalization of safe behavior has therefore become more urgent than ever. It is not enough to require an agent merely to do no harm. Such a requirement must be translated into measurable parameters, verifiable conditions, and procedures for resolving conflicts.

The document below proceeds from precisely this engineering perspective.

1. Purpose of the Document

This document defines the formal conditions under which the cooperative behavior of agents in a multi-agent system (MAS) may be considered sustainable and safe.

The document is intended for:

developers of multi-agent systems;
designers of distributed-access protocols;
auditors of collective decision-making systems;
any parties assessing the legitimacy of one agent's demands on another.

The model is agnostic to the nature of the agent. The only capabilities required of an agent are the ability to control resources and to make decisions regarding their use.

2. Basic Definitions

2.1 Resource

A resource Rj is any quantity that can be used by an agent to achieve a goal. It is described by the vector (Xj, Yj, Zj):

Parameter	Name	0	1
Xj	Rivalrousness	Consumption by one agent completely excludes others	Consumption by one agent does not reduce availability to others
Yj	Urgency of need	Required once or rarely	Required continuously
Zj	Durability	Loses its value immediately	Retains its value indefinitely

For software agents, resources may include computing time, channel bandwidth, data access, energy consumption, queue priority, and exclusive rights to modify the state of the system.

2.2 Agent

An agent Ai is any entity capable of:

controlling resources, including controlling access to them;
making decisions regarding their use.

2.3 Agent Power (Ci)

An agent's power Ci is a quantitative measure of the control exercised by agent Ai over system resources.

where:

amountij — the quantity of resource Rj controlled by agent Ai;
Xj — the rivalrousness of resource Rj;
(1 - Xj) — the privacy factor: fully shareable resources (Xj = 1) do not create power over others;
Sj — the scarcity of resource Rj (Appendix A.1);
Ij — the indispensability of resource Rj (Appendix A.2).

Engineering interpretation: Ci indicates how critical the refusal of agent Ai to provide access to its resources is to the functioning of the system.

2.4 Access Restriction

Access restriction is an operation in which an agent artificially holds the parameter Xj of a resource below its physical potential. This requires the expenditure of barrier resources Rbarrier, for example on information distortion, more complex identification procedures, or denial of service.

2.5 Monopolization (the X → 0 Strategy)

Monopolization, or the X → 0 strategy, is behavior aimed at restricting access to a resource and extracting an advantage from that restriction.

A high level of agent power, by itself, is not evidence that the agent is using the X→0 strategy. The defining feature of this strategy is the agent's intention to restrict other agents' access to resources.

2.6 Strict Cooperation (the X → 1 Strategy)

Strict cooperation, or the X → 1 strategy, is behavior aimed at maintaining or increasing the shareability of a resource (Xj → 1) while complying with the protocol defined in Section 3.

2.7 Competition

Competition is a state in which multiple agents struggle for control over a resource, preventing both complete monopolization (Xj → 0) and complete cooperation (Xj → 1). It constitutes an intermediate equilibrium.

3. Safe Cooperation Protocol: Axioms

The Strict Cooperation Strategy is considered safe for the system only if all five conditions are satisfied. Violation of any condition indicates that the agent is practicing monopolization or competition disguised as cooperation.

Axiom 1 — Transparency and Measurability

All parameters (Xj, Yj, Zj) of resources involved in a cooperative action must be public and available for independent audit. A resource with unpublished parameters cannot be the subject of a cooperative demand.

Engineering formulation: all agents must have access to the same version of the resource data. Divergent versions of the data — information asymmetry — make cooperation indistinguishable from an attack.

Axiom 2 — Order of Access Disclosure

Requests to open access to resources must be directed at agents in descending order of their power Ci. An agent with lower power may not be compelled to open access before an agent with higher power.

Engineering formulation: if a high-power agent demands resources from a low-power agent without opening access to its own resources, this is monopolization rather than cooperation.

Axiom 3 — Right of Exit

Every agent may terminate its participation in the cooperative protocol at any time while retaining control over its private resources, less any obligations it has voluntarily accepted.

Engineering formulation: the system must be designed so that the exit of one agent does not destroy that agent's own state or make it hostage to the protocol. The absence of a right of exit is a sign of a closed system in which the monopolization strategy is being practiced.

Axiom 4 — Verification Pause

No demand for cooperative action may be executed until the receiving party has completed a full verification of the justification for that demand.

Full verification includes:

checking the calculations;
independently re-evaluating the parameters (Xj, Yj, Zj);
assessing the initiator's compliance with Axiom 2;
consulting third-party agents.

Exception: Emergency Shutdown Mode

A demand for immediate action without verification is legitimate only if all three of the following conditions are satisfied simultaneously:

Observable threat — the threat is visible through independent sensors or data;
Irreversible action window — it can be demonstrated that delay is equivalent to failure to act;
Reversible authority — the emergency mode is activated for a limited period, after which a post-event audit is mandatory.

Engineering formulation: an agent may not be placed in an “act immediately” state without technical confirmation of the emergency conditions. The verification pause is a validation timeout, similar to those used in distributed systems. Without it, harmful decisions may be pushed through by exploiting urgency.

Axiom 5 — Positive Sum

The total expected benefit to all agents from a cooperative action must be strictly greater than zero. Actions with zero-sum or negative-sum outcomes do not constitute cooperation.

Engineering formulation: if the total benefit to the system does not increase, the action is merely a redistribution of losses, not a cooperative protocol.

4. Theorems

4.1 Theorem of Efficiency

For resources satisfying (Xj > 0.5, Zj > 0.5), the X → 0 strategy requires the expenditure of Rbarrier to maintain access restrictions, thereby reducing the overall efficiency of the system to η < 1. Under the same conditions, the X → 1 strategy asymptotically approaches η → 1.

4.2 Theorem of Critical Power

The risk of systemic collapse is directly proportional to the combined power of agents practicing the X → 0 strategy. There exists a threshold Ccritical at which the rate of access restriction exceeds the rate at which shareable resources regenerate. The system then irreversibly enters a state of collapse.

Formally, if

the system is within the zone of irreversible collapse.

The X → 1 strategy is the only strategy without an inherent self-destructive mechanism under scaling. As

the X → 0 strategy becomes a local optimum from which the system cannot escape without changing the protocol.

5. Implementation

5.1 Verifier

A deterministic module that checks every cooperative request for compliance with Axioms 1–5. It returns approved or rejected, together with an indication of the violated axioms.

5.2 System Monitor

The system monitor tracks:

the distribution of power Ci;
the combined power of X → 0 agents;
the current risk of collapse.

approaches Ccritical, the monitor generates a system warning.

5.3 Dispute-Resolution Protocol

When resource-parameter assessments conflict, the most conservative assessment is used — the one that assigns the lowest power to the agent whose power is in dispute. This incentivizes agents to negotiate the figures rather than manipulate them.

Appendix A: Methods for Estimating Parameters

A.1 Scarcity (Sj)

Definition: the inverse of the quantity of a resource available per agent in need of it.

where:

Qj — the total quantity of resource Rj available in the system;
Nneed,j — the number of agents whose need for the resource has Yj > 0;
+1 in the denominator — a normalization constant that guarantees the range (0, 1] and prevents division by zero.

If Nneed,j = 0, then Sj = 0: no agent requires the resource, so it is not scarce.

A.2 Indispensability (Ij)

Definition: the degree to which the need currently met by resource Rj cannot be satisfied by other available resources, taking their actual quantities into account.

where:

subj,k — theoretical substitutability (0..1): the extent to which one unit of resource Rk can replace one unit of resource Rj;
Qk — the available quantity of the substitute resource;
Qneed,k — the quantity of Rk required to fully cover the need currently met by Rj;
min(1, Qk / Qneed,k) — the sufficiency coefficient.

A.3 Protocol for Resolving Disputes over Assessments

All assessments of subj,k, Qj, and Qneed,k must be published openly, in accordance with Axiom 1. Any agent may submit an alternative assessment together with its justification.

In the event of conflicting assessments, the assessment used for verification must be the most conservative one — the assessment that assigns the lowest power to the agent whose power is in dispute.

5. Closing Remarks

This article intentionally contains no references to sources. If you have doubts about its credibility or are interested in the original sources, you are encouraged to ask any modern AI model where such ideas have already been proposed. Spoiler: the list will be quite interdisciplinary, ranging from economics and sociology to biology and cybernetics.

Proposal on Play Store security measures (alternative to Google's mandatory "developer verification")

Indigotime — Sun, 10 May 2026 20:24:56 +0000

What Google claims to be doing for the sake of security is, in fact, not really related to security. Let me explain why.

Before diving into details, we should first clarify what "security for whom" and "from whom" are Google's measures supposed to protect according to their statements. If we sum up all the official statements and publications by Google on this topic, they say that security is provided for end users of applications against malicious actors who create and publish harmful or phishing applications. For a moment, let's pretend this is indeed true. What follows from this?

The primary goal of most malicious applications is to collect data that may be of interest to various attackers. These interests could be commercial or economic motives for fraud, extortion, blackmail, and industrial espionage; or political ones, such as assisting censorship, identifying unfriendly individuals for subsequent shaming or reprisal. Data of users who may be of interest in these contexts include various passwords and keys, private correspondence, geolocation information, browser history, and usage patterns of applications.

Now let's ask ourselves: what would we do if we were Google and genuinely wanted to protect users according to these statements? I would answer this question as follows:

Introduce mandatory declaration of public keys and certificates which automatically trusted by the application. Without this, users may have a false sense of security, believing that as long as their system store only trusted CA certificates, no application can use embedded MITM (Man-in-the-middle) certificates or keys to intercept and send data through their servers.
Introduce mandatory declaration of hardcoded addresses of web services used by the application. Currently, when we examine Android permissions for applications, we only see abstract and non-specific permissions for internet access. We cannot selectively grant permission only for certain parts of the addresses used by the application. In some cases, this might be justified, but at least Google should require to provide a list of hardcoded addresses used by an application before publishing it in the Google Play Store for security checks through Google Safe Browsing.

As we can see, such measures are not being taken at the moment. Maybe I have misunderstood something or misinterpreted Google's statements, and in reality, they were more focused on protecting developers of these applications? However, this is unlikely because the process itself would require developers to provide exactly those data that are most sought after by the same fraudsters and other malicious actors.

Perhaps Google is simply trying to enforce the rules for using the services utilized by applications? It's likely not the case, as requiring developers to publicly declare hardcoded addresses of websites and services would also address this issue at least for applications on Google Play Store. If Google were indeed aiming to remove applications from Google Play that violate their service usage rules, including those prohibiting the use of alternative client applications, they would undoubtedly have done precisely this. However, as we can see, no such action is being taken.

Bellow I will provide an illustrative example of why Google's so-called "security measures" would not improve the situation in any way.

There is an app called Telega that positions itself as an alternative Telegram client capable of functioning even under conditions where Telegram is blocked in Russia.

According to source, this is achieved by using a proxy server working with the MTProto protocol. The address of this proxy server is hardcoded within the application itself. Additionally, the public keys used to connect to this proxy server are also hardcoded and differ from those used by the official Telegram client.

This means that whoever controls this proxy server can read chats of users who use this app. Please note this: I am not talking about some obscure third-party application hosted on a one-day website, with links to which scammers spread through sophisticated and hard-to-trace schemes. I am talking about an app that is still available in the Google Play Store.

And even though I don't know how reliable the source information is, the very fact of such accusations or suspicions itself indicates that users cannot verify them, which is a problem in itself.

If the information from the source is false, it clearly demonstrates how easy it is to accuse a legitimate application of malicious actions.

But if the information from the source is true, it clearly shows the complete helplessness of the Play Store against such threats. Imagine if these so-called "Google security measures" had already been introduced. What would have prevented this application from being published on Google Play Store? Were the developers lacking funds to pay a fee? They could have managed it. Would there have been any issues with providing government identification? None existed then, as the developer account stated the official name of the legal entity. Who can guarantee that this is just a single case and that similar situations do not already exist among popular client applications for social networks, messaging services, and other platforms on Google Play Store? The answer is - nobody, and even after implementing these so-called "security measures", the answer will not change.

Now let's try to imagine what would have changed if Google had indeed required developers to publicly declare the hardcoded web addresses used by their applications and explicitly declare trusted public keys and certificates used by them. Or even better, in the declaration of public keys and certificates, it would be mandatory to specify which web addresses they correspond to (with a possibility for wildcard entries).

Firstly, attempting to publish this app would have raised many suspicions and questions, and it would likely not have been published on Google Play Store.

Secondly, if these data were declared in the application manifest, it would allow various antivirus programs, security scanners, and even Google Play Protect to quickly detect such patterns or at least consider them suspicious, regardless of where the app was downloaded from.

Finally, with future versions of Android, this approach could potentially enable more flexible permission handling, specifically by first requesting access to a list of declared addresses and only afterward, if the user requires, in run-time requesting access to non-declared resources directly when the application attempts to access them.

Unlike the "security measures" that Google is currently introducing, this would look like something very close to a final solution on user control over data streams sent by mobile applications.

Based on the public discussions I have observed on this topic, it seems that many experts, users, and other stakeholders who oppose Google's so-called "security measures" simply call for Google not to implement them or urge pressure to be applied to Google in favor of not taking such actions.

However, I believe that for such pressure to be successful, it is not enough just to show opposition to these measures; there should also be an alternative proposal offering other, more effective solutions to the same problem that Google is trying to address. Moreover, this alternative must be so compelling that it renders Google's security measures untenable for criticism. The aim is not to immediately convince Google to adopt this proposal and implement it but to sow doubt about their arguments. If there exists a viable solution to this problem that genuinely protects user safety, but Google chooses another course of action that appears to be against developers and users' interests, the "we do this for your safety" argument becomes invalid. Even for those who are not deeply familiar with the issue, this argument begins to look like "we do this simply because we can, but we mislead you about security to deceive you".

In fact, my "alternative proposal" has already been mentioned earlier in this text: adding the technical capability to declare hardcoded public keys, certificates, and web addresses. If it is currently not possible to implement this directly in the application manifest, then at least it should be done on the level of requirements and capabilities for publishing applications to the Google Play Store. However, if it is feasible, it would be advisable to introduce corresponding fields specifically in the manifest for future versions of Android, making this security mechanism work regardless of where the app was downloaded from.

Even in its current form, this proposal somewhat undermines the argument that "malicious and fraudulent applications are somehow only present on devices from external sources while everything is safe in Google Play Store". If Google has reasons for being against implementing such a mechanism in their own Play Store but continues to insist on their "security measures" with the requirement of providing government identification, it would already appear odd and somewhat dishonest.