Affiliation: Ex-FTC Chair (2021-06-15 to 2025-01-20); Columbia Law School; author of "Amazon's Antitrust Paradox" (2017) One-line position: The AI industry is replicating the concentration patterns of the prior tech era — a few dominant firms controlling compute, models, and distribution — and antitrust enforcement must act now before the market structure locks in.
What she's reacting against
- The "consumer welfare" standard that let Big Tech consolidate unchecked — argues it's inadequate for platform and AI markets
- The assumption that AI partnerships (Microsoft-OpenAI, Amazon-Anthropic, Google-Anthropic) are simply investment, not market control
- Regulatory passivity that waits for harm before acting — advocates for structural intervention before concentration locks in
- Tech industry framing that regulation stifles innovation — argues that concentration itself stifles innovation by starving competitors
Key claims
- AI partnerships are antitrust-relevant: FTC issued 6(b) orders (2024-01) to investigate Microsoft-OpenAI, Amazon-Anthropic, and Google-Anthropic for potential undue influence and lock-in
- Compute concentration is a competition problem: control of key AI inputs (GPUs, training data, cloud infrastructure) by a few firms creates bottleneck power
- FTC report findings (2025-01): Big Tech partnerships create lock-in, deprive startups of key AI inputs, and reveal sensitive competitive information
- "Regulators were too easy on Big Tech": her core thesis — the failures of the 2000s–2010s (allowing platform monopolies) should not be repeated in the AI era
- Structural vigilance, not ex-post remedies: better to prevent concentration than to break it up later
- International coordination matters: FTC + DOJ joint statement with international enforcers on AI competition (2024-07)
Theories aligned with
- Neo-Brandeisian antitrust (structural competition, not just consumer harm)
- Platform economics / gatekeeper regulation
- Adjacent to Mazzucato's public-value frame but from a competition lens rather than industrial policy
Where she overlaps / splits
- Overlaps with Bradford on regulatory ambition for AI; splits on jurisdiction — Khan is US enforcement, Bradford theorizes EU rule-export
- Overlaps with Crawford on AI as a power structure; splits on register — Crawford diagnoses from academia, Khan wielded enforcement power
- Overlaps with Mazzucato on challenging tech industry narratives; splits on mechanism — Mazzucato advocates state investment, Khan advocates competition enforcement
- Splits with Andreessen/Thiel fundamentally — they see antitrust as innovation-killing; Khan sees concentration as innovation-killing
- Splits with Nadella/Pichai on AI partnerships — they frame them as investment; Khan frames them as potential market control
Notable predictions
- (2017) Platform monopolies would persist without structural intervention — outcome: largely supported; Big Tech concentration increased through 2024
- (2024) AI industry partnerships risk replicating Big Tech concentration — outcome: TBD; FTC report provided supporting evidence
- (Implicit) Without enforcement, AI market will consolidate around 3–5 firms controlling compute + models + distribution — outcome: TBD; the structural trend supports this
Track record
- "Amazon's Antitrust Paradox" (2017, Yale Law Journal) reshaped antitrust discourse — arguably the most influential law review article of the decade
- As FTC Chair, launched investigations into AI partnerships, Big Tech mergers, and data practices — institutional action matched rhetoric
- Fired by incoming Trump administration (2025-01) — political vulnerability of the enforcement approach
- Legacy is contested: supporters see a paradigm shift in antitrust thinking; critics see politically motivated overreach
Empirical vs normative
- Empirical: market structure analysis, partnership investigation findings, concentration metrics
- Normative: competition is a public good; structural antitrust intervention is justified before harm materializes; tech-sector self-regulation is insufficient
- Her empirical work (investigation findings) is strong; the normative framework (neo-Brandeisian) is contested within law and economics
Sources
- Law review: "Amazon's Antitrust Paradox" (2017, Yale Law Journal)
- FTC actions: 6(b) orders on AI partnerships (2024-01); staff report on AI partnerships (2025-01); joint statement with international enforcers (2024-07)
- Stanford speech: warning Big Tech over AI (2024)
- Wikipedia: comprehensive biography with tenure dates and major actions
Weak spots / open questions
- Removed from office (2025-01) — her regulatory approach may not survive a different political administration
- Neo-Brandeisian antitrust is academically contested — critics argue it lacks clear consumer-harm evidence and creates regulatory uncertainty
- AI concentration may be driven by genuine economies of scale, not anticompetitive behavior — if bigger labs produce better models, breaking them up could reduce capability
- Less technically engaged with AI capabilities — the frame is market structure, not model behavior or alignment
- Enforcement actions were initiated but many not concluded before her departure — the impact may be institutional (changed the conversation) rather than structural (changed the market)
Rich's take
- (your synthesis here)
converts-from: personas/lina-khan.md · schema v1 · AI & Society domain