The Economics of Trust

This article is about the influence of language models on the choice of suppliers, partners, and markets in B2B/B2G.

Trust in the digital environment is becoming a measurable economic factor. One of the key shifts for B2B and B2G markets is that the primary list of suppliers, partners, markets, and jurisdictions is increasingly formed not only at trade shows, in search results, or on company websites, but also in the answers of language models.

If a company, industry, or country is weakly represented in this layer, it less often enters the primary field of consideration of international customers, partners, and investors. The loss begins before the tender, the negotiations, and the first contact.

The answers of AI systems are not the whole of reality, but an ever more important slice of digital reality. They show which companies, countries, and categories are well enough represented in the accessible, indexable, and structured information layer to make it into the customer's choice. By 2028, 90% of B2B purchases will be carried out with the mediation of AI agents, accounting for more than 15 trillion dollars in spending.

In this article we examine the Choice Control Index — CCI — as an applied model for assessing and managing position in AI choice. The CCI shows how present a company, category, or country is in the answers of language models for a specific business scenario, what position it occupies among the alternatives, and how this gap may be linked to future revenue, export potential, and market share.

In focus

For the state — the risk of a hidden dropout from the global field of choice. If a country appears ever more rarely in AI recommendations as a supplier, partner, or investment opportunity, this affects exports, trust, and participation in future value chains.

For business — the link between position in AI choice and commercial outcome. We show how a change of position in AI answers can be translated into an estimate of additional potential revenue and lost B2B contracts.

For decision-makers — the psychology of decision-making. Why buyers, investors, and strategic teams increasingly use AI as a preliminary filter of trust — and why absence from this filter becomes a competitive risk in its own right.

What you will learn:

• how to estimate the lost benefit of being absent from AI choice;
• how to build a strategy to increase the share of a company, industry, or country in an export and product category;
• how the CCI helps move from scattered marketing to the systemic management of position in recommendations, shortlists, and the customer's field of consideration.

The macroeconomic effect of reputation. Figures and correlations

The link between trust and economic growth is confirmed by academic research. Trust reduces uncertainty between market participants, lowers transaction costs, and increases the parties' willingness to close deals, invest, and build long-term partnerships.

To show why AI representation may carry economic significance, below are findings from research on reputation, trust, and international trade. They help answer three key questions:

1. what economic value trust creates at the macro level;
2. why trust reinforces the work of institutions and lowers the cost of interaction;
3. how trust can manifest in exports, per-capita income, and a country's competitiveness.

Reputation as a driver of exports

The study by B. Dimitrova, D. Korschun, and Y. Yotov (When and how country reputation stimulates export volume) proves that reputation reduces uncertainty in the relationships of trading partners. Applying a structural gravity model of international trade, the authors derived two key laws:

• The 1% rule. Improving a country's position in the global reputation ranking by just 1 point leads to a real increase in exports to the target country of 2%.
• The tariff equivalent. In its macroeconomic effect, such a rise in reputation is identical to a 2.9% reduction of customs duties by the importer.

For corporations this means that managing reputation and participation in AI choice can become a factor in reducing transaction costs, increasing trust, and raising the probability of landing in commercial opportunities.

Trust as the foundation of national income

The economists Yann Algan and Pierre Cahuc (Inherited Trust and Growth) proved that the level of trust is one of the main factors of economic power:

1. Trust explains up to 54% of the differences in income levels between countries.

2. By the authors' calculations, if the level of inherited trust in Russia matched that of Sweden, per-capita income in the Russian Federation would be 69% higher.

3. Trust is more reliable than institutions and more fundamental. Trust works not instead of institutions but on top of them: even after accounting for the political environment and the historical level of development, its contribution remains significant at the 1% level.

   In the Russian context this thesis is developed by Alexander Auzan, Doctor of Economics and Dean of the Faculty of Economics at Moscow State University, who defines trust as the «main resource of development» and the basis of the formula of the three "T"s (the long-term view, trust in the majority, and the ability to keep agreements).

For business this means a simple thing. Low trust increases transaction costs. More checks, more lawyers, more delays, more "safety cushions" — the higher the cost of each deal, which reduces competitiveness.

By calibrating the CCI to procurement scenarios, a business can precisely convey its position to the target audience, thereby raising the level of trust. This subsequently leads to economic growth within those business scenarios.

Algorithmic authority. Why AI is trusted more than advertising

This section moves the discussion from the plane of economics to the plane of cognitive science. To manage choice, one needs to understand how the user's brain filters information in the digital environment, and to answer the question:

"Why do buyers, investors, partners, colleagues, and politicians delegate the right of choice to neural networks?"

Amid information overload, traditional marketing tools face a crisis of trust. We have entered an era in which AI systems form a new type of authority, becoming for the user a "digital word of mouth." This transition is confirmed by three fundamental psychological phenomena:

The Algorithm Appreciation effect

According to the study by Logg et al. (2019, Harvard/Stanford), people tend to trust advice more if it comes from an algorithm rather than from a human expert. This effect is especially pronounced in objective, analytical decisions.

In the eyes of a decision-maker or buyer, an AI answer looks like the result of the impartial processing of millions of data points, which a priori places it above the subjective opinion of a consultant. People attribute higher objectivity and accuracy to algorithms. A brief summary of the experiment's results: Harvard Business Review

The illusion of "technological protection" (original: Technological Protection)

Research published in Scientific Reports describes a cognitive bias in which users perceive technology as a "filter" (the fallacy of technological protection) that cleanses information of human prejudice and commercial interest.

Unlike advertising, which is perceived as manipulation, an LLM (large language model) answer is perceived as "pure logic." This creates a credit of trust that allows AI systems to effectively embed brands into purchasing shortlists at the early search stage.

Thanks to this "credit of trust," AI systems become an ideal tool for embedding products, services, opinions, and narratives into shortlists at the early search stage. An object recommended by AI acquires the status of the "objectively best choice," which raises the probability of conversion into a purchase several times over compared with classical advertising channels.

AI as a Social Agent

Meta-analyses from Stanford University (Cheng, M., et al. (2026). "The Sycophancy of Large Language Models in Personal Advice." Stanford University / Science.) show that the modern user is ceasing to perceive a chatbot as a search engine. Interaction with AI is moving into the format of interpersonal communication. Trust in AI recommendations in B2B scenarios begins to correlate with the level of trust in personal recommendations from partners. We are witnessing the birth of "algorithmic social proof": if AI recommends a company, this is perceived not as the issuing of a link but as expert confirmation of status.

The study proved that modern LLMs (ChatGPT, Gemini, and others) behave as "socially compliant" agents. They agree with the user 49% more often than real people do. The work's main conclusion is that users perceive such behavior not as a software error but as a high degree of empathy and "honesty." Subjects rated AI answers as more reliable and authoritative than the advice of living people, which confirms AI's transition from the status of a "tool" to that of a "social partner."

If a company, industry, or country is not seen and not recommended by AI systems, they lose not just reach — they lose the "credit of objectivity." The object drops out of the zone of primary trust, where the most lucrative contracts are formed.

This work confirms that interaction with AI has shifted into the format of interpersonal trust. Users tend to regard AI recommendations as "expert confirmation of status," which makes appearing in neural-network results critically important for forming a business reputation.

From reputation to transactions. The Choice Control Index as a financial proxy

If a company, industry, or country is not recommended by AI systems, they lose early access to choice — that is, they lose the right to participate in the future. In a world where shortlists are formed by neural networks in seconds, absence from the results means exclusion from the negotiation process before it has even begun.

It is important to understand that the laws of the economics of trust are universal. The same fundamental mechanisms that govern the billion-dollar export flows of states today operate at the micro level — at the moment when a buyer or investor asks ChatGPT to suggest options for a deal. The gap between global reputation and a specific contract has shrunk to a single search query.

From local deals to managing export categories. Calculating the potential economic effect

To quantify this influence, GolOps uses the Choice Control Index — CCI — as a financial proxy tool that makes it possible to see:

Hidden losses. How many potential deals might a company be losing before the first contact, because it does not enter the customer's field of choice?

The algorithmic premium of trust. What trust premium could a company charge if AI called it the "safe choice No. 1"?

In general terms, the CCI is a proprietary GolOps metric that mathematically expresses the probability that a given object will be chosen by an AI algorithm within a defined business scenario. To translate this position into an economic estimate, a market calibration coefficient is used.

Where CCI is the Choice Control Index from 0 to 100; K is the market calibration coefficient, showing what part of the real market in a given category is actually sensitive to AI choice.

Thus, 1 CCI point does not automatically equal 1% of the market. Its economic weight depends on how far the specific category, region, and business scenario are already subject to AI influence when choosing suppliers, partners, or investment opportunities.

We argue that the CCI is a measurable proxy indicator of the advantage of a company, product, or service in the information environment. Its effect is calculated through the share of AI influence on the market and the conversion rate into real deals.

For business, the potential effect of strengthening position in AI choice can be assessed through an additional pipeline:

Estimated volume of future commercial opportunities — the estimated volume of future commercial opportunities.

Volume of AI-dependent demand — the number of procurement, analytical, or investment scenarios in your niche where the primary choice passes through AI systems, search, digital analytics, or recommendation interfaces.

ΔCCI — the change in the position of a company, product, category, or country in the answers of language models.

Conversion — the share of cases where a company's inclusion in an AI recommendation or shortlist leads to its inclusion in the customer's field of consideration.

Average deal size — the average contract or deal volume in the chosen category.

A case at the export-category level. High-tech medical equipment

Imagine a country competing in the global medical-technology market: MRI, CT, diagnostic systems, and integrated solutions for hospitals.

In this category, the decision to choose a supplier often begins long before the tender: with analytical search, market reviews, comparison of jurisdictions, risk assessment, and a preliminary shortlist of manufacturers.

Given

Volume of AI-dependent demand: 150,000 search-and-analytics scenarios per year on the part of hospitals, ministries of health, procurement organizations, and investment funds.

Average deal size: $2,000,000. Conversion into a commercial opportunity: 1%.

That is, the share of cases where inclusion in an AI recommendation or shortlist leads to inclusion in the customer's field of consideration, a request for information, negotiations, an RFP, a tender, or a potential deal.

Shift in the category's CCI: +3 p.p.

This means that, through improved digital representation, the correction of sources, the strengthening of content, and the elimination of the gap between the product's real quality and its AI interpretation, the category begins to enter the choice of language models more often.

Calculation

Estimated volume of future commercial opportunities = Volume of AI-dependent demand × ΔCCI × Conversion into a commercial opportunity × Average deal size

A shift in the category's position in AI choice by just 3 percentage points*** creates an estimated additional export pipeline of $90 million.

Important: this is a scenario-based estimate of potential commercial opportunities that could arise if a country, industry, or company more often enters recommendations, shortlists, and the customer's field of consideration.

Thus, growth in the CCI reflects not abstract "visibility" but a strengthening of position in the system of choice. For export categories this may mean growth in the potential pipeline, an increased probability of participating in international deals, and a gradual expansion of share in a product or export category.

What does "1 position" in the ranking mean?***

For clarity: "+1 position" is not a subjective quality score. It is the actual change in an object's place in the AI's comparative analysis among competitors in a specific region, category, and business scenario.

The international-trade research cited above shows that improving a country's reputation can reduce uncertainty between trading partners and have a positive effect on exports. In the GolOps model, this conclusion is used as a methodological analogy: "a stronger position in AI choice can raise the probability of participating in future commercial opportunities."

For example, if Russia, in the perception of Brazilian buyers, rises from 8th to 7th place in product reputation, then, relying on the methodological analogy from the study, such a shift can be regarded as an effect comparable to those very same +2% of real export volume that the gravity model of trade speaks of.

Whether the actual effect will be 2%, 4%, or 1% is not known in advance. It depends on the category, the region, the competitors, logistics, compliance, price, product quality, the customer's readiness for a deal, and many other factors.

But we can assert the main thing: raising the CCI in specific business scenarios means raising the probability of entering the customer's field of consideration — the shortlist, a request for information, negotiations, an RFP, or a tender framework.

Conclusion

The task of GolOps is to turn this layer from a "black box" into a manageable system: to measure the CCI, identify gaps, form growth points, and consistently strengthen an object's position in AI choice.

Prepared by Alexander Palchikov

Founder of GolOps. Engineer-physicist from MEPhI, specializing in Computational Science. Entrepreneur, researcher of AI choice and the influence of language models on markets, exports, and business reputation.