Filed under industrial-policy
Every brief tagged "industrial-policy".
- § 01 · AI-AGENTS
Tokens Meter Input, Not Value: Hong Yanqing on Beijing's Agent Measures (Part 4 of 4)
Part 4, closing Hong Yanqing's commentary on the Several Measures of Beijing Municipality on Accelerating Agent-Led Development (北京市关于加快智能体引领发展的若干措施, 京发改〔2026〕1185号). The Measures' Article 6 proposes a Token (词元) economy — Token-as-a-Service, Agent-as-a-Service, Results-as-a-Service, and a shift from billing by Token consumption to value-based billing; Article 8 funds 'Token factories' and Token vouchers. Hong draws the line the policy still needs: Tokens measure the consumption of intelligent means of production, not the value of intelligent products. Tokenization differs across models; a task's full cost includes tool calls, memory storage, human review, and failed retries; and Token volume has no fixed ratio to task value — so treating Token throughput as industrial performance rewards long contexts, loops, and retries. His alternative is a five-layer evidence chain (resource input → system capability → valid task results → process results → enterprise and social value), a cost-per-valid-completed-task formula that counts review, retries, and expected risk losses, and an attribution discipline of pre-launch baselines and phased pilots. Outcome billing must be corrected for quality and risk — narrow metrics make customer-service agents rush calls and procurement agents chase price cuts, and vendors can cream-skim easy tasks while humans absorb the hard residue — so projects with unstable task boundaries should blend base, resource, and performance fees rather than jump to pure Results-as-a-Service. Different policy objects need different-layer metrics, mapped onto Part 3's five maturity levels, and fiscal support should pass staged evidence gates: prototypes may fail, pilots must beat baselines in real business, demonstrations must replicate at acceptable cost, and commercial-stage projects must survive subsidy taper — with prompt exit for projects that stop producing new evidence.
- § 02 · AI-AGENTS
How Agents Actually Enter the Enterprise: Hong Yanqing on Beijing's Agent-Led Development Measures (Part 1 of 4)
Part 1 of Hong Yanqing's three-part commentary on the Several Measures of Beijing Municipality on Accelerating Agent-Led Development (北京市关于加快智能体引领发展的若干措施, 京发改〔2026〕1185号, issued 21 July 2026). Hong maps agent supply along two axes — who builds and operates (enterprise self-build, standardized third-party products, joint co-construction with forward-deployed engineers, public/industry shared platforms) and how capability is delivered (whole solutions, componentized assembly via skill marketplaces, embedded in existing software and terminals) — and argues Beijing has covered supply almost completely. What the Measures have not yet answered is adoption: enterprise demand is not 'an agent' but a definable, delegable, verifiable task, and between agent supply and enterprise production processes stand six institutional thresholds — unformed procurement-ready demand, processes that lack the standardization agents require, blocked access to data and tools, missing authorization and responsibility regimes, procurement and acceptance mechanisms built for conventional software, and the absence of migration and exit capability. His prescription: the next phase of Beijing agent policy should pivot from expanding supply to promoting adoption — maturity assessment and process diagnosis, open and non-discriminatory agent access to enterprise software, capability-permission-responsibility inventories, first-purchase programs tied to real production tasks, staged funding tied to task outcomes rather than Token volume, and risk-sharing, insurance, and business-continuity mechanisms for early adopters.