When AI becomes infrastructure: Sam Altman's vision in an interview with Rowan Cheung and the reorganization of the digital economy

The unstoppable change doesn't begin tomorrow, it's already underway – but very few people notice it in time

The days when artificial intelligence was considered a futuristic technology are over. What Sam Altman outlined in his interview with Rowan Cheung in early October 2025 is no longer a vision, but rather an assessment of an already underway transformation. With 800 million weekly active users, ChatGPT has reached the critical mass necessary to evolve from a product to a platform. The five central theses from this conversation—ChatGPT as a distribution platform, the Agent Builder as a democratization tool, the vision of zero-person companies, AI-driven scientific breakthroughs, and the normalization of synthetic media—mark turning points in how companies will create, distribute, and scale value in the future. This analysis examines the historical roots of this development, its current mechanisms, and the strategic implications for companies that want not only to survive but to thrive in this new era.

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• YouTube interview with Rowan Cheung: Sam Altman on AI companies without staff, Sora, AGI breakthroughs and more

The evolution of distribution models: From app stores to conversational ecosystems

To understand the significance of ChatGPT as a distribution platform, it's worth looking at the history of digital distribution channels. The breakthrough of the iPhone in 2007 and the introduction of the App Store in 2008 created a completely new paradigm: software was no longer sold in stores, but discovered and downloaded from digital marketplaces. Apple controlled distribution and took a 30 percent cut of every transaction. This model became the blueprint for almost all subsequent platforms.

The next evolution came with social networks like Facebook, which enabled distribution not through a separate store, but directly within the news feed. Advertising became the dominant business model because attention was captured where users already were. The principle: Bring the functionality to where users are, instead of sending them to a separate location.

ChatGPT now marks its third evolutionary stage. At DevDay 2025, OpenAI not only unveiled new models but also initiated a fundamental shift in thinking. With the Apps SDK, developers can integrate interactive applications directly into the chat. Users can create Spotify playlists, search for properties with Zillow, or design with Canva without ever leaving ChatGPT. The conversation itself becomes the interface, the operating system, and the distribution platform. This development differs fundamentally from the previous GPT Store, which existed as a separate element. Now, apps are seamlessly embedded in the conversation flow. OpenAI is thus pursuing the iOS strategy: control over the intelligence layer, provision of developer tools, and distribution via a massive user base of 800 million weekly active users.

Historical developments reveal a clear pattern: Each new platform reduces the friction between intention and execution. The App Store reduced friction with physical stores, social networks reduced it with separate apps, and ChatGPT now reduces it to natural language. You no longer need to know which app you need—you simply state what you want to achieve.

Parallel to this development, business models have evolved. While early software companies relied on license sales, subscriptions and ad-based models later dominated. OpenAI is now introducing a new dimension with the Agentic Commerce Protocol: transactions can be completed directly within the chat. Instant checkout enables purchases without any breaks in the user experience. This creates a new category of commerce that is neither e-commerce nor social commerce, but rather conversational commerce. Companies that are not present in this ecosystem risk losing touch with a massive user base. In the first few weeks after the announcement of the Apps SDK, over 50,000 developers registered. This dynamic is reminiscent of the early days of the iPhone, when developers realized that a new platform was emerging on which they needed to be present.

The strategic relevance for companies is immense. If you can't be found in chat today, you simply don't exist for a growing number of users. The question is no longer whether you need a website or an app, but whether you have a conversational presence. Distribution is being rethought – away from funnels, SEO, and app store optimization, towards natural language discoverability and contextual relevance.

The Agent Builder: Democratizing Automation and Its Disruptive Consequences

The second key thesis from Altman's interview concerns the massive lowering of the barrier to entry for building AI agents. With Agent Builder, OpenAI has created a visual, no-code tool that enables any knowledge worker to build, test, and deploy autonomous agents. This democratization is not merely a marketing phrase, but a fundamental shift in who can shape automation.

Historically, automation has always been the domain of specialists. The industrialization of the 18th and 19th centuries required engineers and mechanical engineers. The digitalization of the late 20th century needed programmers and IT departments. While Robotic Process Automation (RPA) of the 2010s reduced the technical requirements, it still remained a tool for dedicated teams. Agent Builder radically breaks with this tradition. A marketing manager can build an agent that generates weekly reports. A sales representative can configure an agent that generates proposals. A lawyer can develop an agent that reviews contracts for specific clauses. The barrier between idea and implementation is reduced to a minimum.

This development follows a familiar pattern from software history: abstraction enables scaling. As programming languages ​​evolved from machine code to higher-level languages, more people could develop software. When spreadsheets evolved from VisiCalc to Excel, millions of non-programmers could perform complex calculations. Agent Builder is the next stage of this abstraction. It abstracts not only code, but entire workflows, decision logic, and integrations.

The implications are far-reaching. Over the next twelve months, companies will be intensely focused on using agents. Not because it's technologically fascinating, but because their competitors are doing it. Early adopters are already reporting significant productivity gains. The Spanish bank BBVA created over 2,900 customized Global Processing Tasks (GPTs) in six months, and 80 percent of users report weekly time savings of more than two hours. These figures may seem conservative, but multiplied by thousands of employees, they translate into massive efficiency gains.

In the interview, Altman emphasized that the average knowledge worker can now build their own agents. The consequence: Each department can develop its own automations without relying on central IT resources. This leads to a decentralization of innovation. Automation is no longer determined by the IT budget, but by the initiative of individual teams. The competitive advantage lies with those who experiment quickly. Companies still waiting for perfect, centrally orchestrated solutions will be overtaken by agile teams that start with simple agents and iteratively improve them.

However, this development also carries risks. Decentralized agent development can lead to fragmented processes, security gaps, and governance challenges. Who is allowed to use which data? How are agents audited? What quality standards apply? Companies must develop frameworks that enable innovation without losing control. The successful organizations will be those that find a balance between experimentation and governance, between speed and security.

Agent Builder also sends a signal to the software industry. Tools like Zapier, Make, or traditional RPA solutions face the challenge that their core function—workflow automation—is now being integrated directly into conversational interfaces. The question is not whether these tools will disappear, but how they need to reposition themselves to remain relevant.

From One-Person to Zero-Person Companies: The Reorganization of Value Creation and Work

The third thesis is the most provocative: Altman spoke of a bet among tech CEOs on when the first zero-person company worth one billion dollars would emerge. Originally, the bet was for the first one-person trillion-dollar company. But the development is progressing faster than expected. Altman predicts that this could become a reality in years, not decades.

To grasp the scale of this, one must consider the historical development of company size and value creation. In the industrial era, revenue and number of employees were strongly correlated. More production required more workers. The digital era began to break this correlation. Instagram was sold to Facebook for one billion dollars in 2012—with 13 employees. WhatsApp reached a valuation of 19 billion dollars in 2014—with 55 employees. These examples demonstrate that software and network effects can generate extreme leverage.

The next stage involves one-person businesses scaling through AI agents. The entrepreneur uses agents for customer service, marketing, product development, sales, and finance. This vision sounds futuristic, but is already technologically feasible to a certain extent. AI can write code, create designs, compose marketing copy, answer customer inquiries, and analyze data. The limiting factors are no longer primarily technical, but more strategic: What problem are you solving? For whom? And how do you reach this target group?

Altman goes a step further: zero-person companies. Agents that operate autonomously, make decisions, allocate resources, and create value – without human involvement in day-to-day operations. People wouldn't disappear, but rather shift into orchestrating, strategic roles. They define goals, set parameters, and monitor results. Agents handle the execution.

This vision raises fundamental questions. If an agent can run a company, what remains as a human contribution? Altman argues that human drive, creativity, and judgment don't disappear but flow into new areas. Work shifts from executing to shaping, from reacting to visioning. But this transformation is not painless. Entire job profiles become obsolete. Knowledge workers, whose activities primarily consist of information processing, face the challenge of redefining their role.

In the interview, Altman used an interesting metaphor: A farmer 50 years ago would probably not perceive today's office work as real work. Farming produces food, something essential for survival. From this perspective, many modern jobs appear like games to fill time. This pattern could repeat itself in the AGI era. Future generations might perceive our current work as less real than what they consider meaningful.

This philosophical dimension touches upon the fundamental question: What is work? And why do people work? If material needs can be efficiently met through AI and automation, the question shifts from necessity to meaning. People will continue to strive for significance, recognition, and self-actualization. However, the ways in which this occurs will change dramatically.

For companies, this means that the competitive advantage of the future is not the idea itself, but the speed with which it can be implemented using agents. Traditional scaling required capital, talent, and time. AI agents reduce all three. Less capital is needed because operating costs decrease. Talent is needed differently—less for execution, more for strategy. Time is reduced because agents work 24/7, don't get tired, and can be replicated quickly.

The consequence: markets become more dynamic, competitive advantages more short-lived, and barriers to entry lower. Established companies must ask themselves how they can adapt their processes, cultures, and business models to a world in which a small team with intelligent agents can disrupt a market they have dominated for decades.

AGI signal: When machines create new knowledge

The fourth thesis concerns a qualitative leap: AI is beginning to make genuine scientific discoveries. Altman described this as the moment when AI no longer merely reorganizes existing knowledge, but generates new knowledge – novel discovery. This ability is a crucial characteristic of Artificial General Intelligence.

Historically, scientific progress was an exclusively human endeavor. Researchers formulated hypotheses, conducted experiments, analyzed data, and drew conclusions. Machines provided support—for example, through calculations or simulations—but the creative, hypothesis-generating steps remained human. This boundary is increasingly blurring.

DeepMind's AlphaFold revolutionized protein folding by predicting structures that would have taken humans decades to create. MIT's generative AI models designed new classes of antibiotics effective against resistant bacteria. OpenAI's o3 and Gemini Deep Think achieved gold medal-level results at the International Mathematical Olympiad—not through rote memorization, but through independent problem-solving. These examples demonstrate that AI is increasingly capable of navigating uncharted territory and finding original solutions.

Altman emphasized that this development is only just beginning. He predicts that AI will achieve scientific breakthroughs in fields such as medicine, materials science, and physics in the coming years. These breakthroughs will not only be incremental but could potentially change fundamental paradigms. If AI can conduct research faster and more precisely than humans, scientific progress will accelerate exponentially.

The implications for companies are enormous. Research and development cycles are shortening. Pharmaceutical companies can discover and develop new drugs more quickly. Material manufacturers can simulate new alloys or plastics before they are produced. Energy companies can design more efficient batteries or solar cells. The competitive advantage is shifting from whoever has the most resources to whoever uses the most intelligent systems.

But this transformation also raises ethical and strategic questions. If AI makes scientific discoveries, who owns them? The company that operates the AI? The AI ​​developer? Society? The answers to these questions are unclear and will be the subject of intense debate in the coming years.

Furthermore, the role of human researchers is changing. Instead of conducting experiments themselves, they are becoming curators, hypothesis generators, and interpreters. They define research questions, evaluate results, and set ethical boundaries. The work is becoming more creative and strategic, less routine and repetitive. This necessitates a reorientation of education. Scientists must learn to collaborate with AI systems, understand their strengths and limitations, and develop their own complementary skills.

Altman made an interesting prediction: Humanity will become accustomed to AI-driven scientific breakthroughs. Initially, there will be a two-week period of excitement, then the discovery will become commonplace. This normalization process is characteristic of technological progress. What seems extraordinary today will be taken for granted tomorrow. The challenge for companies is to internalize this speed of change and adapt their strategies accordingly.

Synthetic Media: When Reality and AI Blur

The fifth thesis concerns synthetic media and the rapid normalization of AI-generated content. Altman described how strange it initially felt to watch Sora-generated videos—and how quickly that strangeness dissipated. After three minutes, it was simply an app full of generated videos. This speed of normalization has profound consequences for brands, media, and society.

Historically, producing media content was complex and expensive. Photographs required cameras, films required studios and crews, and music required instruments and recording equipment. These barriers ensured a certain degree of quality control and authenticity. With digital technology, these barriers gradually diminished. Smartphones enabled everyone to create photos and videos. Social media platforms enabled everyone to share them. Yet, despite this democratization, a core of authenticity remained: a photograph depicted something that existed in front of the camera.

Synthetic media fundamentally break with this assumption. Sora 2 can generate videos that are photorealistic but were never actually filmed. Faces, voices, scenes – everything can be synthesized. OpenAI introduced the Cameo feature, allowing users to embed their own face and voice into AI-generated videos. This opens up creative possibilities but also carries significant risks.

Deepfakes are already a well-established problem. Manipulated videos of politicians, fake celebrity endorsements, synthetic pornographic content without the consent of those depicted – the potential for misuse is manifold. OpenAI is attempting to counter these risks with multi-layered security measures. Prompt filters block the generation of content featuring politicians or celebrities without permission. Every Sora video carries digital watermarks and metadata that identify it as AI-generated. Classifiers and human moderators monitor generated content.

Despite these measures, a residual risk remains. Reality Defender demonstrated that Sora's security mechanisms can be circumvented. In tests, they managed to pass deepfakes of prominent figures through verification, while their own detection tools identified them with over 95 percent accuracy. This shows that the security of synthetic media is an arms race between protective measures and attempts to circumvent them.

For companies, this means that clear AI guidelines and brand safety processes are becoming essential. Brands must define how they use synthetic media—and how they ensure that their brand values ​​are not damaged by manipulated content. Transparency is becoming a key principle. Users need to know when content is AI-generated. Regulations such as the EU AI Act already require the labeling of synthetic media. Companies that proactively set transparent standards build trust. Those that neglect this risk reputational damage.

At the same time, synthetic media offer enormous creative and economic opportunities. Marketing campaigns can be personalized: a video that varies slightly for each viewer to appear more relevant. Product visualizations can be created in seconds, without expensive photoshoots. Training content can be automatically translated into different languages ​​and cultural contexts. The productivity gains are immense.

Altman emphasized the need to boldly test new content formats. Companies that rely on tried-and-tested methods will be overtaken by those that experiment. The challenge lies in balancing innovation and responsibility. Those who are too cautious miss opportunities. Those who are too careless risk scandals.

The societal dimension should not be underestimated. If anyone can create photorealistic videos, trust in visual media erodes. What was once considered proof—a photo, a video—is becoming increasingly questionable. This has implications for journalism, the justice system, and public discourse. Organizations must develop mechanisms to verify authenticity. The Coalition for Content Provenance and Authenticity is working on standards for digital proof of origin. Companies that support and implement such standards contribute to stabilizing the digital ecosystem.

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Practical implementation: How companies integrate the five viewpoints

The theoretical insights are valuable, but practical implementation is crucial. Two concrete use cases illustrate how companies are already using the five statements.

The first example comes from the financial sector. The Spanish bank BBVA implemented ChatGPT Enterprise, enabling employees to create their own GPTs. Within six months, over 2,900 customized applications were developed. Legal departments use agents for contract review, marketing teams generate personalized campaigns, and financial analysts automate reporting. The result: 80 percent of users save more than two hours per week. Distribution occurs directly within the work environment—employees don't need to open separate tools but work within the familiar ChatGPT interface. The challenge lies in the integration with existing systems. BBVA is working on connecting ChatGPT to internal databases to enable even deeper insights. This example demonstrates how the democratization of agent development and the platformization of ChatGPT work together to achieve massive efficiency gains.

The second example comes from the automotive industry. Toyota uses AI-powered predictive maintenance to reduce downtime. Sensors on production equipment collect data that is analyzed by AI models. These models identify patterns that indicate impending failures and enable preventive maintenance. The result: a 25 percent reduction in downtime, a 15 percent increase in overall equipment effectiveness (OEE), and annual cost savings of ten million dollars. The return on investment (ROI) was approximately 300 percent. This example illustrates how AI can not only optimize administrative processes but also be integrated into physical production environments. AI's ability to extract insights and make predictions from vast amounts of data corresponds to the fourth claim: AI generates new knowledge—in this case, about when machines are likely to fail.

Both examples demonstrate common success factors. First: a culture of experimentation. Companies that give employees the freedom to experiment with AI tools discover useful applications more quickly. Second: governance frameworks. Without clear guidelines on data protection, security, and quality, risks arise. Third: an iterative approach. Expecting perfect solutions from the outset is unrealistic. Instead, companies should start with simple applications, learn, and continuously improve. Fourth: integration. AI tools reach their full potential when they are seamlessly integrated into existing workflows, rather than existing as separate islands.

Controversies and critical debate: Risks of the brave new world

As promising as these five theses are, they also raise significant questions and controversies. The first concerns job losses. If agents take over tasks previously performed by knowledge workers, what happens to these people? Altman's argument that work is transforming is optimistic, but not without its critics. Historically, technological upheavals have created new jobs, but often not quickly enough or in the same sectors. The transition phase can cause social disruption. Goldman Sachs estimates that AI automation of knowledge work could save $1.5 trillion in labor costs globally—a euphemism for potential job losses. Companies and societies will need to develop retraining programs, social safety nets, and new educational concepts to manage this transition.

The second controversy concerns the concentration of power. OpenAI controls ChatGPT, a platform with 800 million users, and is building an ecosystem on it encompassing developers, users, and transactions. This concentration is reminiscent of the market power of Google, Apple, or Amazon. The danger: OpenAI could dictate terms, increase fees, or favor certain developers. Regulatory authorities are observing this development with increasing scrutiny. Antitrust investigations could follow. Companies that rely heavily on ChatGPT risk becoming dependent on a platform whose future is uncertain.

The third controversy concerns deepfakes and disinformation. Despite security measures, synthetic media can be misused. Political manipulation, financial fraud, defamation – the risks are real. OpenAI's own tests showed a 1.6 percent error rate in blocking rule-violating sexual deepfakes. Even small error rates can lead to thousands of problematic pieces of content among millions of users. Society must develop detection technologies, legal frameworks, and educational programs to deal with this new reality.

The fourth controversy concerns data privacy and surveillance. AI agents need access to data to work effectively. Companies must ensure that sensitive information remains protected. OpenAI's enterprise offerings promise not to use company data for training public models. However, trust in such promises still needs to be established. Furthermore, there is a risk that widespread use of AI will lead to a culture of surveillance in which every action is documented and analyzed.

The fifth controversy concerns environmental impact. Training large AI models requires enormous computing power and therefore energy. OpenAI is investing heavily in data centers and chips. Sam Altman himself shifted his focus to acquiring more computing capacity. This expansion has an ecological footprint. Companies using AI should consider sustainability aspects and seek energy-efficient solutions.

These controversies demonstrate that the transformation Altman describes is not simply progress. It brings challenges, risks, and ethical dilemmas. Companies must act responsibly, create transparency, and actively contribute to finding solutions.

Future prospects: Trends and potential upheavals

What developments can we expect in the coming years? First, further democratization. No-code and low-code tools will become even more accessible. The barrier to building your own AI applications will continue to fall. This will lead to an explosion of applications, but also to fragmentation and quality issues. Platforms that offer curation, quality assurance, and integration will become more valuable.

Second, autonomy levels are increasing. Agents will be increasingly able to autonomously complete tasks lasting several days or weeks. Altman suggested that Codex could soon handle a week's work autonomously. This further shifts the role of human workers toward oversight, strategy, and creativity. Work becomes less transactional and more transformational.

Third: Multimodality is becoming the standard. GPT-5 and Sora 2 demonstrate that AI understands and generates not only text, but also images, videos, and audio. Future systems will seamlessly switch between these modalities. A user could describe a concept, and the AI ​​could generate a video, a design document, and a presentation from it—all in one go.

Fourth: Personalization at an individual level. AI will increasingly be able to understand the preferences, learning styles, and contexts of individual users and adapt responses accordingly. This leads to hyper-personalized experiences but also raises questions about filter bubbles and manipulation.

Fifth: Regulation is intensifying. Governments worldwide are working on AI legislation. The EU AI Act, Chinese regulations, US initiatives – all aim to minimize risks and promote innovation. Companies must not only comply with these regulations but also actively participate in shaping them to create workable frameworks.

Sixth: New business models are emerging. Conversational commerce, AI-as-a-Service, agent marketplaces – the monetization of AI is becoming more diverse. Companies that experiment early can secure first-mover advantages.

Seventh: Hybrid human-AI teams are becoming the norm. The future is not human versus machine, but human with machine. The most successful companies will be those that optimize this collaboration. This requires new leadership concepts, organizational structures, and cultural change.

Eighth: Hardware integration. Altman is working with Jony Ive on new devices. When AI is integrated into wearables, smart glasses, or other form factors, the way we interact with technology will fundamentally change. The conversational interface will become ubiquitous, always available, and context-aware.

Synthesis: Recommendations for action for the new era

The five viewpoints from Altman's interview are not isolated trends, but converging forces reshaping the foundations of the digital economy. ChatGPT, as a distribution platform, is changing where and how companies reach their target audiences. Agent Builder is democratizing automation and shifting innovation from centers to individuals. Zero-person companies are challenging the relationship between labor and value creation. AI-driven scientific breakthroughs are accelerating research and development exponentially. Synthetic media are opening up creative possibilities, but require strict ethical guidelines.

This results in clear areas of action for companies. First: Experiment. Launch small AI pilot projects, learn, and iterate. Waiting is not an option. Second: Build governance. Establish frameworks for data protection, security, ethics, and quality before problems arise. Third: Develop talent. Employees must learn to work with AI, leverage their strengths, and develop complementary skills. Fourth: Form partnerships. No company can handle everything alone. Ecosystems, collaborations, and open standards are crucial. Fifth: Take responsibility. Transparency towards customers, fair treatment of employees, and contributing to societal solutions—companies must consciously shape their role in the transformation.

The era Altman describes is not a distant future, but an unfolding present. The winners will not be the largest or most established companies, but the most adaptable. Those that learn quickly, experiment boldly, and act responsibly. The transformation from productivity to creativity, from tools to infrastructure, from human-led to human-orchestrated—it is happening now. And every company must decide: shape it or be shaped by it.

Who is Rowan Cheun?

Rowan Cheung is a Canadian entrepreneur, tech communicator, and one of the most influential voices in the field of artificial intelligence. He is the founder and CEO of The Rundown AI, the world's fastest-growing AI newsletter with over 350,000 subscribers and millions of followers on social media. Originally from Vancouver, British Columbia, he has established himself since 2023 as a key media figure, presenting AI knowledge in an understandable, accessible, and strategic way.

Cheung didn't begin his career in technology, but as a competitive swimmer. After health setbacks during the COVID-19 pandemic, he turned to the world of technology and AI, teaching himself the ropes. Within a year, he learned to program and subsequently founded Supertools, a database platform for AI applications with over 250,000 monthly users. His content and analysis on developments in generative AI, automation, and AI-powered businesses quickly made him a fixture in the global tech scene.

In 2023, he won the "Twitter Growth Challenge" as the world's fastest-growing tech communicator on Platform X (formerly Twitter). Today, he is among the ten most influential tech founders on social media – in a category with figures like Elon Musk, Gary Vaynerchuk, and Sam Altman.

In addition to his media projects, Rowan Cheung hosts the podcast "The State of AI," in which he regularly interviews leading technology figures, including Sam Altman, Mark Zuckerberg, and Jensen Huang. The podcast and the newsletter "The Rundown" are now considered key information sources for managers, entrepreneurs, and developers in the field of AI.

Cheung is known for his practical perspective on AI: how companies can achieve concrete productivity gains, how agents can be used in the workplace, and how individuals can scale through AI without building large teams. In interviews, he regularly emphasizes that his small team of around 15 employees operates like a 50-person company thanks to intelligent AI workflows.

In summary, Rowan Cheung represents the new generation of AI founders: self-taught, data-driven, extremely online-savvy, and with the ability to translate complex technological developments into concrete, applicable strategies for businesses.

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