AI Must Advance: From Intelligence Scale to Value-Creation Scale. AI has reached a remarkable moment in human history. Intelligence, computing power, models, agents, and automation are advancing faster than most institutions can absorb. Yet every transformative technology eventually faces a larger question: what will the world do with it?
Computing became indispensable because the world found millions of uses for it. The Internet became transformative because billions of people found reasons to connect, communicate, transact, and create through it. AI now faces the same historical test, not whether it can become more intelligent, but whether the world can discover an equally enormous purpose for that intelligence. There is no room left to discuss its artificiality or wisdom, as it is now all about real value creation.
What is the larger human purpose of AI?
At what scale can AI be deployed into real productive activity?
And where will the enormous new demand for AI capabilities come from?
The above questions increasingly sit beneath the AI conversation. These are not questions for one laboratory, one technology company, or one government. They belong to the entire AI ecosystem. The extraordinary achievement of building increasingly capable intelligence is only one side of the equation. The other side is finding an equally extraordinary field of human activity in which that intelligence can continuously create value. The next AI discussion needs to move beyond capability to purpose, deployment, and demand.
The corporate world is already experimenting with AI across thousands of functions. Documents are being processed, software is being generated, information is being analyzed, customer interactions are being automated, and increasingly sophisticated agents are being developed. But doing thousands of small tasks inside a large organization is not necessarily the same thing as running a large operation.
The next question is much larger: Can AI begin to understand and orchestrate a mandate, coordinate an operation, respond to changing conditions, and produce measurable outcomes? If AI is entering its agentic era, the natural progression is from task execution to operational execution, from assisting the organization to increasingly becoming part of how the organization itself operates.
That transition creates a second question that is rarely discussed at comparable scale: Where will AI find enough real-world operating environments to learn, deploy, and create value? The answer may not be found only inside the world’s largest corporations. It may exist in the enormous entrepreneurial population operating beneath them. Millions of enterprises make, repair, design, transport, grow, sell, export, service, and trade every day. Each one is a living operating environment containing customers, suppliers, workers, products, decisions, risks, opportunities and accumulated experience. Taken together, the world’s SMEs may represent one of the largest distributed productive environments available to AI.
This is where Expothon brings a globally focused option to the discussion: 100 million SMEs as a potential next-scale AI deployment environment. This is not a proposal to put another software package into 100 million businesses. It is a different proposition altogether. What if 100 million entrepreneurial operating environments could progressively become AI-enabled? What if AI could encounter millions of different business problems, markets, products, customer behaviors and operational decisions simultaneously? What if the SME universe became not merely a market for AI, but one of the largest places where AI could discover how intelligence becomes practical value?
Read more by Naseem Javed: AI: The Unanswered Questions (September 29, 2026)
There is another reason this proposition matters. AI possesses extraordinary explicit knowledge and computational capability. SMEs possess something profoundly different: tacit knowledge. The entrepreneur knows what the customer actually means, which supplier will deliver, which product will fail, where a margin disappears, what local market is changing, which employee can solve a problem, and when an apparently good opportunity is not worth pursuing. Much of this knowledge is never formally documented. It lives inside experience and judgment. AI does not need to replace that knowledge. The opportunity is to meet it, augment it, and multiply it. AI brings procedural processing at machine scale; entrepreneurs bring execution knowledge at human scale.
That combination could change what AI deployment means. Instead of asking only how many people use an AI application, the world could begin asking how many enterprises can use AI to create something new. A small manufacturer could discover a new export market. A repair business could diagnose problems more intelligently. A food producer could optimize production and distribution. A family enterprise could digitize knowledge accumulated over generations. A local entrepreneur could gain capabilities once available only to a multinational corporation. Multiplied across millions of enterprises, these are not merely productivity improvements. They could become a new architecture of distributed value creation.
This is why the next stage of AI may require a different definition of scale.
Stage One is intelligence scale: larger models, greater computing capacity, broader knowledge, faster inference, more capable agents, and increasingly sophisticated systems.
Stage Two could be value-creation scale: millions of enterprises using that intelligence in millions of different circumstances to create products, services, markets, productivity and prosperity. The first stage makes intelligence more powerful. The second determines how widely that intelligence can become economically and socially productive. The transition from one to the other may be one of the defining questions of the AI era.
Expothon is not entering this discussion as another AI camp, nor is it asking AI leaders to accept a predetermined answer. It is offering a global question and an open field for discussion.
What would it take to connect AI with 100 million SMEs?
What architecture, infrastructure, expertise, platforms, policy frameworks, and entrepreneurial mobilization would be required?
What would AI need to become capable of doing at that scale?
And what new demand for AI could emerge if 100 million enterprises were actively looking for ways to turn intelligence into value?
Read more by Naseem Javed: BRICS 2026: Economy will sacrifice elections (September 15, 2026)
The question is deliberately larger than any one institution.
It requires entrepreneurs’ tacit knowledge to meet AI’s explicit knowledge.
The world’s AI leaders have already demonstrated that extraordinary intelligence can be built. The next historical opportunity may be to discover how extraordinary intelligence becomes extraordinary global value creation. Sam Altman, Jensen Huang, Elon Musk, Dario Amodei, Demis Hassabis, Satya Nadella, Mark Zuckerberg and the wider AI leadership community do not need another lecture about the technology they are building. They need a larger field of human execution in which that technology can prove its next potential.
One hundred million SMEs could become such a field.
Expothon therefore offers an invitation: let the AI camps, governments, corporations, and entrepreneurial world sit together and examine whether AI can move into its second stage, from intelligence scale to value-creation scale, from extraordinary computing to extraordinary human possibility.
If the dominant AI intellectual culture is overwhelmingly populated by people whose expertise is explicit knowledge-building intelligence, it is natural that the conversation gravitates toward: models → compute → algorithms → agents → benchmarks → autonomy.
Who possesses the tacit knowledge required to turn those capabilities into millions of commercially useful outcomes? That is where the 100-million-SME proposition becomes much more than an SME argument. It becomes an argument about the division of knowledge required for AI’s second stage.
The first AI era may be built by the geeks.
The second AI era may have to be built with the entrepreneurs.
So, the extraordinary irony may be: AI is being developed to absorb and manipulate enormous quantities of explicit knowledge, while the world’s largest reservoir of entrepreneurial knowledge remains largely tacit.
China makes the question harder to ignore
If a country begins thinking about AI+SME = Grassroots Prosperity as a national-scale deployment problem, the question changes from:
Why isn’t the AI world studying the 100 million SME population as one of the world’s largest potential AI operating environments, with so many powerful SME sectors around the world?
Where is the AI conversation about economic growth coming from the grassroots upward, through the world’s SMEs?
THE TOP 10 AI LEADERS
Prime Topics: Public Long-Form Media & Podcast Discussion Scan
| AI Leaders | SME Topics | 100 million SME? | Tacit Knowledge | Grassroot Growth | AI next Stage |
| Altman | ○ | — | — | — | — |
| Huang | ○ | — | — | — | — |
| Amodei | ○ | — | — | — | — |
| Hassabis | ○ | — | — | — | — |
| Zuckerberg | ✓ | — | — | ○ | — |
| Nadella | ○ | — | — | ○ | — |
| Musk | ○ | — | — | — | — |
| LeCun | ○ | — | — | — | — |
| Li | ○ | — | — | ○ | — |
| Wang | ○ | — | — | — | — |
Source: CHT public long-form media/podcast scan, September 2026
✓ Direct/substantial ○ Limited/adjacent — No clear formulation identified
The rest is execution.