Algorithmic pricing, data dominance and acquisition-driven elimination of rivals are exposing gaps in Vietnam's competition policy. The paper urges a Digital Economy Unit, dominance tests that capture data and network effects, transaction-value merger thresholds and tailored rules to protect competition while preserving innovation and green transition goals.

Main Finding

The digital economy introduces four core disruptions to traditional competition—algorithmic collusion, data-power abuse, self-preferencing, and killer acquisitions—that expose critical gaps in existing competition law. Addressing these requires tailormade regulatory design (beyond market-share metrics and price-focused analysis), new procedural tools (e.g., transaction-value thresholds), and institutional capacity (a Digital Economy Unit) to balance competitive markets with innovation and sustainability goals—especially important for emerging digital economies such as Vietnam.

Key Points

• Four key digital-era competition problems identified:
  • Algorithmic collusion: automated pricing and coordination can produce coordinated outcomes without explicit agreements, complicating detection and enforcement.
  • Data-power abuse: strategic control and aggregation of data create de facto barriers to entry and sustained market power.
  • Self-preferencing: platforms can advantage their own services, undermining fair competition in multi-sided markets.
  • Killer acquisitions: incumbents acquire nascent rivals primarily to eliminate future competition, often escaping merger control because targets have low revenues.
• Legal and analytical gaps in current competition frameworks:
  • Defining relevant markets is hard when strong network effects and multi-sided dynamics exist.
  • Traditional price- and revenue-based tests fail in zero-price or low-price platform markets.
  • Consumer-welfare metrics need adaptation to account for non-price harms (privacy loss, reduced quality, innovation deadweight).
  • Tension between enforcing competition and preserving incentives for innovation and investment.
• Policy recommendations (Vietnam-focused but generalizable):
  • Create a Digital Economy Unit inside the Competition Authority to build technical expertise and coordinate cross-sector policy.
  • Broaden dominance criteria to include data control, network effects, and gatekeeper characteristics, not just market share.
  • Introduce specific prohibitions or ex ante rules for algorithmic coordination, self-preferencing, and data-exclusionary practices.
  • Adopt transaction-value thresholds (not only revenue thresholds) for merger review to capture high-value, low-revenue target acquisitions.
  • Use flexible, technology-aware remedies and regulatory sandboxing to adapt to rapid innovation.
• Link to sustainability:
  • Platform concentration can either accelerate green innovation (through scale and coordination) or hinder it (by blocking entrants with sustainable alternatives). Competition law reform can be aligned with sustainability objectives.

Data & Methods

• Interdisciplinary approach combining:
  • Economic theory: conceptual modeling of platform markets, network effects, and data externalities.
  • Legal analysis: review of competition law doctrine, enforcement gaps, and merger control practice.
  • Technological assessment: evaluation of algorithmic pricing, data architectures, and platform governance features that enable anti-competitive conduct.
• Analytical methods are primarily conceptual and policy-oriented (synthesis of literature, legal gap analysis, and techno-economic assessment). The study uses these to identify actionable regulatory design choices rather than relying on new microdata estimation.

Implications for AI Economics

• Measurement and enforcement:
  • AI-driven pricing and personalization shift the locus of anti-competitive harm away from observable price squeezes to algorithmic coordination and quality manipulations—requiring new detection metrics (e.g., algorithmic behavior audits, counterfactual simulations).
  • Data concentration is a central source of market power in AI markets; antitrust analysis must integrate data access, quality, and exclusivity as competitive inputs.
• Merger policy:
  • “Killer acquisitions” are especially salient in AI: incumbents gain from acquiring small model/algorithm/ML teams before they scale. Transaction-value thresholds and forward-looking innovation tests are needed.
• Market definition and welfare:
  • Standard price-based consumer-welfare models are often inadequate for AI-enabled platforms; welfare assessments should incorporate data privacy, platform quality, choice, and innovation dynamics.
• Regulatory instrument set:
  • Beyond ex post antitrust: ex ante regulations (gatekeeper duties, interoperability/data portability mandates), algorithmic transparency/audits, and institutional specialization (Digital Economy Unit) improve enforcement feasibility.
• Innovation-competition trade-offs:
  • Policies must be calibrated to avoid chilling innovation: flexible remedies, sandboxing, and proportional rules can protect competition while allowing experimentation.
• Sustainability nexus:
  • AI economics should consider how platform design and data governance influence green innovation diffusion; competition policy can be a lever to accelerate sustainable technologies by ensuring entrant access and preventing exclusion of eco-innovations.

If you want, I can convert these recommendations into a short policy brief for the Vietnam Competition Authority or map specific regulatory instruments (e.g., data-portability rules, algorithmic audit protocols, merger thresholds) to implementation steps and potential enforcement indicators.