OpenAI keeps a key figure secret: guess the real economic impact of AI

Artificial intelligence (AI) fascinates and worries at the same time. For several years, this revolutionary technology has promised to disrupt the economy, transform work, and increase global productivity. Yet, despite colossal investments and intense media coverage, the secret surrounding the key figures of its real economic impact persists. OpenAI, a pioneer in the field, jealously guards an essential figure, leaving analysts and economists speculating about the true consequences of this innovation. AI, far from being an immediate panacea, raises the crucial question of the extent to which it truly influences economic growth and the distribution of wealth in the digital market. This economic mystery, at the heart of debates in 2026, invites a deep reflection on the tangible or illusory effects of artificial intelligence on our societies.

OpenAI and the secret around a key figure: what implications for the economy?

OpenAI, a major player in artificial intelligence, holds crucial data on the economic impact of its technologies but chooses to keep certain key figures confidential. This secrecy fuels curiosity and debates around the real economic performances of the innovations it offers. Indeed, the pressure is strong: investors, governments, and economic actors seek to understand how much AI truly contributes to the Gross Domestic Product (GDP), job creation, or the transformation of production chains. But why does OpenAI restrict access to this information?

A first explanation may be linked to the disruptive nature of AI. OpenAI is at the heart of an innovation that could profoundly change the global economy. By keeping a key performance figure secret, the company can better control market perceptions and the reactions of governments or competitors. This gives it a major strategic advantage, allowing it to define the contours of its influence without revealing all its cards.

But this secrecy also hides a certain complexity in evaluating the economic impact. Artificial intelligence is not reducible to a simple growth or revenue figure. Its effect is inseparable from a multitude of factors: company adaptation, job evolution, infrastructure investments, not to mention social externalities such as wealth redistribution. By keeping this secret, OpenAI highlights the difficulty of immediately and precisely measuring the scale of AI in the real economy.

In this context, the secret around a key figure from OpenAI more broadly symbolizes the challenge that economic transparency represents in an era dominated by technology and rapid innovation. Digital markets evolve at such a speed that it becomes difficult, for all stakeholders, to assess the true fallout of AI on growth, especially when these technologies spread heterogeneously across sectors.

Finally, this secrecy impacts the trust of economic actors and the general public. Without clear data, it is more complicated to anticipate labor market transformations, prepare public policies, or effectively regulate the sector. OpenAI, aware of this responsibility, therefore plays a determining role in how the contribution of AI to the global economy will be perceived, analyzed, and ultimately integrated.

The economic reality of AI in the United States: between massive investment and limited returns

The rush around artificial intelligence is notable, but the economic reality it generates in the United States tempers some enthusiasm. Indeed, despite unprecedented investments, the American economy does not show a spectacular surge in terms of productivity or employment directly linked to AI. For example, in 2025, nearly $410 billion were injected into automation and artificial intelligence technologies. Yet, experts like Dario Perkins from TS Lombard see no convincing proof that these investments have improved real economic growth.

Large banks, often carriers of pragmatic caution, illustrate this trend. Goldman Sachs, after closely observing the effects of AI, downgraded its forecasts, estimating that expected productivity gains remain absent for now. These findings are based on field surveys, value chain analyses, and a rigorous observation of macroeconomic figures. Brian Peters, former regulator at the Federal Reserve Bank of New York, shares this view. Despite promises, immediate economic fallout remains difficult to prove.

Several factors explain this gap. Firstly, investments are often global by nature. An American company may acquire semiconductors made in Taiwan or outsource activities to other countries, which dilutes the local impact. This means that the direct economic effect of investment by a company does not automatically translate into national GDP.

Secondly, individual productivity may improve, but that does not guarantee a systemic transformation of supply chains. Thus, gains remain contained at the internal level of companies without immediate creation of a wider ripple effect, delaying visibility on the overall economy. This phenomenon is often referred to as the “productivity paradox”, where perceived benefits far exceed those measured by traditional indicators.

This observation invites caution in interpreting AI-related figures, especially when they are used to predict major economic changes. Technology, to be truly transformative, requires time, adaptation, and a complete reconfiguration of economic processes.

Investments and prospects: how is AI shaping the economic future?

In 2026, investments in artificial intelligence continue to rise, with forecasts that could reach $660 billion for the year. This movement reveals a lasting confidence of companies in the disruptive potential of the technology. But these impressive figures must not overshadow the challenges accompanying this revolution.

Massive investment illustrates the quest for long-term efficiency. For companies, adopting AI mainly means optimizing operations, reducing costs, and opening new horizons for innovation. For example, in manufacturing, AI helps predict breakdowns, improve predictive maintenance, and automate repetitive processes, resulting in significant competitiveness gains.

Moreover, the services sector, notably finance and health, increasingly relies on artificial intelligence to refine diagnostics, personalize the customer experience, and streamline transactions. But even in these areas, the advent of AI requires a skills update and a cultural change, which can slow its full integration.

Forecasts also rely on the vision of economists and experts, including Aaron “Ronnie” Chatterji, chief economist at OpenAI. According to him, the economic impact of AI will follow the model observed during other major technological revolutions like electricity or the Internet. These technologies took years, even decades, to see their influence reflected in economic statistics, reflecting the need for gradual deployment and a total reorganization of work modes.

The challenge is therefore less immediate than structural. AI acts as a catalyst for profound transformation, where the direct measurement of its effects requires a broad perspective, integrating market adaptation, legislation, and training. This dimension complicates the role of economic decision-makers and regulators who must support the transition without hindering innovation.

The differentiated impact of AI on economic sectors: innovation and disruption

The digital economy is enriched with a multitude of artificial intelligence applications, each having varying effects depending on the sector. Some industries have already observed impressive improvements, while others remain in the experimental or cautious phase.

In finance, AI revolutionizes risk management, algorithmic trading, and personalized customer service. For example, certain investment funds use sophisticated algorithms to anticipate market trends with unprecedented speed and precision. This pushes for increased competitiveness but also raises issues related to financial stability and transparency.

In manufacturing, AI materializes through autonomous robots, predictive maintenance software, and automated quality control systems. These innovations make processes more efficient but require heavy investments and a reorganization of human teams. The balance between productivity gains and social impact on employment remains delicate.

Healthcare services benefit from artificial intelligence through medical data analysis, early disease detection via machine learning, or personalized treatments. However, large-scale deployment is hindered by regulatory, ethical, and data privacy issues.

Finally, the commerce and logistics sector sees the rise of intelligent systems for inventory management, automated delivery, or consumer behavior analysis. These applications facilitate smooth exchanges and customer satisfaction but involve rapid and constant digitalization of infrastructures.

In summary, the impact of AI is as diversified as the sectors concerned, each market facing its own challenges and opportunities for innovation or disruption.

Key examples of sectors impacted by AI

• Finance: predictive analytics, algorithmic trading, risk management
• Manufacturing: autonomous robots, predictive maintenance, quality control
• Healthcare: AI-assisted diagnostics, personalized treatments, medical data management
• Commerce and logistics: intelligent stock management, automated delivery, customer analysis

Decoding a paradox: does AI really generate inclusive growth?

OpenAI’s secret regarding certain key figures of the economic impact reveals a broader concern about artificial intelligence’s ability to generate shared benefits. Indeed, one of the main criticisms raised concerns how growth driven by AI is distributed within the economy and society.

OpenAI has also warned about a scenario where productivity growth, facilitated by AI, would mainly benefit a handful of actors, thus deepening inequalities. This perspective challenges the widely held idea that technological progress automatically means shared prosperity. The concentration of income in the hands of digital giants who dominate the AI market generates a significant economic and social divide.

This observation urges a more serious look at questions of redistribution, access to know-how, and education. How to ensure that the benefits of technological innovation do not only benefit the elites? Dialogue between companies, public authorities, and civil society becomes essential to implement policies that support employees and adapt economic systems.

In the background, OpenAI’s secrecy about its key economic figures highlights a desire to control these issues, without avoiding the debates. Transparency, as well as social responsibility, appears as a sine qua non condition for AI to become a force for collective progress rather than a factor of division.

The challenges of measuring AI’s economic impact: between complexity and delays

Measuring the economic impact of artificial intelligence proves to be a complex exercise. OpenAI keeps certain key figures secret because reality often exceeds simple expectations around growth and productivity figures. Classic indicators such as GDP or productivity do not always faithfully reflect the progress induced by AI.

Economist Aaron “Ronnie” Chatterji, from OpenAI, emphasizes that the effect of artificial intelligence is similar to that of historical breakthrough technologies like electricity or the Internet. These revolutions did not transform the economic landscape overnight. They required years, even decades, of gradual deployment, integration into industrial processes, and transformation of organizational modes.

This dynamic thus explains why economic gains are not immediately measurable. Companies must deeply rethink their work methods, train employees on new tools, and adapt their strategies to a rapidly changing environment. This delay, often called the “productivity paradox,” illustrates the gap between displayed potentials and observed results.

This phenomenon invites analysts and decision-makers to a nuanced, long-term reading of economic evolution. Technology companies must better communicate about these dynamics, while public institutions have a crucial role to play in supporting this transition and ensuring a relevant assessment of the impacts.

Table: comparison of AI investments and economic impacts in different countries

Country	AI Investments (in billion $)	Estimated GDP Contribution (%)	Main impacted sectors
United States	410 (2025)	1.2%	Technology, finance, industry
China	320 (2025)	1.5%	Manufacturing, e-commerce, finance
European Union	250 (2025)	1.0%	Health, energy, transport
Japan	150 (2025)	0.8%	Automotive, robotics, industry

Ethical and social challenges of artificial intelligence in the digital economy

The massive integration of artificial intelligence technologies in the economy raises many ethical and social questions, often overshadowed by capitalist logic and enthusiasm for innovation. OpenAI, while keeping certain key figures confidential, also warns about the need for increased attention to these issues to ensure responsible AI deployment.

Data protection, algorithm transparency, and bias prevention are at the heart of current debates. The risk is that AI reproduces or amplifies existing inequalities, either in access to technology or in its use within organizations. For example, if tools are designed without diversity in their development teams, they can inadvertently discriminate against certain social groups.

On the employment front, fears of massive job substitution by automation are real. Yet, AI can also become a lever to support workers by relieving them of repetitive or arduous tasks and allowing them to focus on higher value-added activities. Success will depend on how companies and regulators manage this human transition.

Finally, AI innovation governance is a global challenge. It involves reconciling powerful economic interests with societal needs by establishing an appropriate regulatory framework that fosters ethical, sustainable, and inclusive innovation. OpenAI, through its strategic choices, participates in this crucial reflection, especially since its secretly held key figures can influence public and political perception.