HKPC launches 'AI for All' program to help SMEs and workers adopt technology

TEXT START: The Hong Kong Productivity Council launched its “AI for All” Inclusive Programme on Thursday, offering training and practical support to help small and medium-sized enterprises, workers and different community groups make greater use of artificial intelligence.

The Dissection

This is transition management dressed as inclusion. The program distributes training, workshops and access while avoiding the central consequence: AI adoption is valuable precisely because it reduces the amount of human labor required for the same output. “AI for All” means the tools are being democratized; it does not mean the gains, ownership or bargaining power will be.

The article inventories courses, cloud-company participation, sector applications and responsible-use language. It describes diffusion, not distribution. SMEs and workers are being prepared to operate inside an AI-shaped economy whose productive assets remain controlled by firms, platforms and infrastructure owners.

The Core Fallacy

The core fallacy is treating technological adoption as synonymous with worker empowerment. Training can improve an individual’s ability to use AI, but it cannot guarantee that the individual remains economically necessary after the tool automates the task.

Under the Discontinuity Thesis, the decisive chain is cognitive automation dominance, institutional inability to preserve human-only work at scale, and collapse of productive participation. Better prompting, data analysis and workflow optimization may make workers more efficient while simultaneously making fewer workers necessary. The program may accelerate the mechanism it presents as protection.

Hidden Assumptions

• That displaced tasks will reliably become new durable jobs rather than being absorbed by automation.
• That SMEs will use AI mainly to augment staff instead of reducing headcount, outsourcing expertise or intensifying workloads.
• That “changing job requirements” can be managed through retraining without changing the ownership structure of AI capital.
• That access to courses equals access to productive leverage.
• That responsible-use principles—privacy, bias and transparency—address the economic problem of dispossession. They do not.
• That Hong Kong can preserve stable human economic domains while global AI competition rewards substitution and cost reduction.
• That broader adoption will distribute productivity gains instead of concentrating them among firms controlling models, data, compute, energy, logistics and maintenance.

Social Function

Primary classification: transition management and ideological anesthetic, with a partial truth.

The partial truth is real: AI literacy will matter, and some participants will gain temporary advantage. The anesthetic is the implication that universal access converts structural displacement into universal opportunity. It does not. It creates a larger pool of competent users competing for a shrinking quantity of human-essential work.

The program is also a coordination device for institutions that need to appear responsive while the labor-to-consumption circuit deteriorates. “Inclusive” training absorbs anxiety, produces measurable activity and delays open recognition that productive participation—not merely consumption—may be collapsing.

The Verdict

This is not a solution to AI displacement. It is a state-supported onboarding program for the next labor regime. It may help selected SMEs and workers survive the transition, but its aggregate effect is likely to widen AI adoption faster than it preserves human necessity. The courses distribute operational literacy; they do not distribute sovereignty. Under DT mechanics, that is hospice care for the wage system with better branding.