2025-10-01 03:23:02
Yes, I will be doing a Conversation with him. He is a tried and true New Yorker like few others, a very successful investor and head of XN, a long term focused investment firm that invests across public and private markets. He is a “generational investor,” one of the most successful over the last twenty years. He is also an avid collector of contemporary art, and he serves on the Boards of Trustees of The Mellon Foundation, The Whitney Museum of American Art, The Trust for Governors Island, and The Institute for Constitutional Advocacy and Protection and is a member of The Council on Foreign Relations. And more. Here is Google on Gaurav Kapadia.
So what should I ask him?
Do not forget — in equilibrium the lesser-known guests probably are on average better.
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2025-10-01 02:00:40
London has slipped out of the world’s top 20 initial public offering markets as the third quarter ends, overtaken by Mexico and Singapore in a fresh blow to its standing as a global finance hub.
The UK exchange has slipped three places to 23rd in a Bloomberg ranking of the world’s busiest IPO destinations, placing it behind the frontier market of Oman. Volume this year dropped 69% to $248 million, the weakest haul in more than 35 years.
This year’s largest London IPO — an April offering from accountancy MHA Plc — raised £98 million ($132 million). No deals have involved a major Wall Street bank; they were instead arranged by small local outfits like Cavendish Plc and Singer Capital Markets. The third-quarter picture is even starker with just $42 million of deal volume, down 85% from the same period last year, according to data compiled by Bloomberg.
Here is more from Bloomberg. Once your status as a financial center unravels…
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2025-09-30 23:54:41
1. Dean Ball on classical music.
2. Profile of Russell Vought (NYT).
3. Research center markets in everything.
4. “Don’t fret about life extension, you’ve already absorbed everything that really matters.” Link to the piece here.
5. Seb Krier on AI as a means of obliterating transactions costs.
6. Did the Chinese exam system benefit imperial unity?
The post Tuesday assorted links appeared first on Marginal REVOLUTION.
2025-09-30 12:49:33
Let’s do leaf-blowers next!:
More than 42 million Americans are exposed to medium or high levels of traffic noise. Despite its potentially large toll and unequal distribution, the economic costs, incidence, and policy implications of traffic noise have received limited attention in economics. We quantify the aggregate economic burden of this externality and its distribution across demographic groups by estimating homebuyers’ willingness to pay for quieter environments. Using quasi-experimental variation from the construction of noise barriers, we find that reduced traffic noise exposure leads to significant increases in house prices, implying that buyers are willing to pay a substantial premium for each decibel of noise reduction. In the five years before construction, we detect no differential pre-trends in prices between treated and control properties. Following construction, we observe an immediate and largely permanent 6.8% increase in prices within 100 meters, with smaller gains at greater distances. Information on each barrier’s noise attenuation allows us to recover the willingness to pay per decibel of traffic noise. We calculate the aggregate economic cost of traffic noise at $110 billion nationwide. The economic burden is disproportionately borne by lower income and minority households, suggesting that the externality is regressive. The cost varies widely across cities, reflecting differences in noise levels, property values and population density. Based on our estimates, the socially efficient Pigouvian tax amounts to $974 per vehicle. A broad shift to electric vehicles — which are quieter than traditional vehicles — could yield noise reduction benefits of $77.3 billion, concentrated among low-income families in dense urban areas.
That is from a new NBER working paper by Enrico Moretti and Harrison Wheeler. Via tekl.
The post The costs of traffic noise appeared first on Marginal REVOLUTION.
2025-09-30 12:31:16
My prompt:
I read that ChatGPT will be starting connections with Shopify, Stripe, and perhaps other companies for LLM commerce, as you might call it. That is, you could search for something in the app and then buy it through GPT. OpenAI would receive some sort of commission, as Google does now if you buy something through a Google search. Using standard theories of tax incidence, provide an analysis of a) optimal pricing strategy for OpenAI and its partners, and b) where will the incidence of those new fees fall? On consumers? Retailers? Taken away from Google? Other?
The Claude 4.5 answer is here under the fold, sadly the extensive footnotes and citations do not transfer:
# LLM Commerce Economics: Market Structure, Pricing Strategy, and Tax Incidence Analysis
**OpenAI’s entry into transactional commerce on September 29, 2025 through partnerships with Shopify, Stripe, and Etsy marks a fundamental shift in digital commerce intermediation.** With an estimated 2% commission rate—dramatically lower than Google Shopping’s historical 12% or Amazon’s 15%—OpenAI’s model raises critical questions about optimal pricing strategy, competitive positioning, and where the economic incidence of new commerce fees will ultimately fall. The answer depends on elasticities, market power dynamics, and the structural differences between LLM-mediated and search-based commerce.
## OpenAI’s announced partnerships reveal aggressive market entry pricing
OpenAI officially launched “Instant Checkout” on September 29, 2025, enabling direct purchases within ChatGPT conversations through confirmed partnerships with three major platforms. **Stripe serves as the foundational payment infrastructure partner** (since March 2023), co-developing the open-source “Agentic Commerce Protocol” that enables encrypted payment tokens across different processors. **Etsy went live immediately** with single-item purchases from U.S. sellers available to ChatGPT Plus, Pro, and Free users. **Shopify integration is rolling out to over 1 million merchants** including major brands like Glossier, SKIMS, Spanx, and Stanley 1913.
The business model operates through commission-based revenue, though specific rates remain confidential. **Sam Altman publicly hinted at approximately 2% affiliate fees** in a Stratechery interview, positioning this as an alternative to traditional advertising models where OpenAI would “never take money to change placement.” This represents market-entry pricing 83% lower than Google Shopping’s former 12% commission rate and 87% lower than Amazon’s typical 15% referral fees.
**OpenAI emphasizes that merchants remain the merchant of record**, retain customer relationships, and control fulfillment—a significant departure from Amazon’s model. The company explicitly states that fees do not influence product ranking or placement, with products ranked by relevance to user intent, availability, price, quality indicators, and whether Instant Checkout is enabled. This positions OpenAI’s model closer to affiliate marketing than traditional marketplace economics, but with substantially greater influence over the consideration set through AI curation.
The technical architecture matters for competitive dynamics. The Agentic Commerce Protocol is open-sourced and platform-agnostic, working across payment processors beyond Stripe. This suggests OpenAI aims to establish infrastructure standards rather than create a closed ecosystem—potentially enabling rapid scaling while reducing merchant lock-in concerns that might otherwise dampen adoption.
## Current commerce market structure shows high but declining commission rates
The online commerce landscape OpenAI enters is characterized by **dominant players charging substantial take rates** and **emerging competitive pressure** from social commerce platforms. Understanding existing commission structures and market shares is essential for predicting where OpenAI can extract value and how competitors will respond.
**Amazon maintains 37-40% of U.S. e-commerce** with a complex fee structure that effectively extracts 30-35% of transaction value when including referral fees (8-45% by category, typically 15%), FBA fees, and advertising costs. The company generated nearly $600 billion in GMV in 2024 and over $50 billion in advertising revenue, demonstrating how marketplace power enables layered monetization beyond transaction fees.
**Google fundamentally restructured its commerce model** by eliminating commissions entirely for “Buy on Google” (previously 5-15%, averaging 12%) to compete with Amazon and social platforms. Google now relies exclusively on advertising revenue from Shopping Ads using cost-per-click pricing. This strategic pivot acknowledges that Google’s advantage lies in discovery rather than transaction completion, and that charging commissions reduced merchant adoption.
**Social commerce represents the fastest-growing segment** at 30-36% CAGR, reaching $1.16-1.63 trillion globally in 2025. TikTok Shop charges 6-8% commissions (most categories 6%), while Instagram and Facebook charge 5% per shipment plus 2.9% processing fees. These platforms are capturing impulse purchases and discovery-driven shopping, particularly among younger demographics.
**Traditional affiliate networks** operate on variable commission structures ranging from 5-30% depending on product category, with Amazon Associates paying 1-10% (averaging 3-5%). YouTube Shopping charges approximately 15% median commissions. These models demonstrate that recommendation-based commerce has historically commanded premium rates compared to search-based discovery.
The competitive landscape shows **declining commission rates due to platform competition and merchant bargaining power**. Google’s elimination of transaction fees, TikTok’s aggressive 6% pricing, and the proliferation of direct-to-consumer channels via Shopify have created downward pressure on take rates. OpenAI’s ~2% estimated commission must be understood in this context—it’s not just below-cost pricing for market entry, but recognition that merchants now have credible alternatives.
## LLM commerce fundamentally differs from search in ways that affect elasticities
The economic analysis of commission incidence requires understanding how LLM-mediated commerce differs structurally from traditional search, as these differences directly impact price elasticities, switching costs, and market power—the key determinants of tax incidence.
**User intent and conversion dynamics diverge significantly**. ChatGPT shopping users demonstrate 8% higher engagement, 12% more pages per visit, and 23% lower bounce rates compared to traditional search traffic. However, Adobe Analytics data across 1 trillion+ visits shows generative AI traffic is initially 9% less likely to convert than other sources, despite these engagement advantages. The trajectory matters: shopping queries on ChatGPT doubled from January to June 2025, and click-through rates tripled from 2.2% to 5.7% in the same period, suggesting rapid improvement as users develop comfort with the channel.
This creates a **high-consideration, lower-immediate-conversion funnel** fundamentally different from search’s high-intent, direct-conversion model. The economic implication: LLM commerce currently excels at the discovery and consideration stages where it reduces search costs for consumers, but underperforms at transaction completion due to trust and habit factors. This bifurcated purchase journey means LLM platforms may capture influence value without capturing transaction volume, creating misalignment between contribution to sale and commission collection opportunity.
**Trust factors introduce significant friction with economic consequences**. State-of-the-art LLMs achieve approximately 80% accuracy on benchmarks, meaning one in five recommendations could contain errors. Unlike traditional search where users see multiple sources and can verify independently, LLM commerce presents synthesized, curated results where verification asymmetry creates information problems. This trust gap appears in behavior: 46% of shoppers report being unlikely to let AI manage their entire shopping trip according to Walmart surveys, and 72% of commerce leaders cite privacy and security as moderate to major roadblocks for AI implementation.
The trust deficit has direct implications for elasticities. When consumers don’t fully trust recommendations, they’re more likely to conduct additional research or comparison shopping before purchasing, reducing the price insensitivity that platforms need to extract surplus. Trust must be built through accuracy and transparency, creating a time dimension to elasticity estimates—current elasticities may not reflect long-run behavior once trust develops.
**Integration depth creates new switching costs absent in search**. Traditional search imposed low switching costs: users could easily try different search engines, and no accumulated personalization locked them in. LLM commerce creates multiple lock-in mechanisms through personalization-driven data accumulation. The system learns user preferences through conversation history, product interactions, and stated requirements, creating a bilateral learning dynamic where both user and system lose accumulated knowledge when switching platforms.
Quantified impacts demonstrate these effects: AI-powered sales convert at double the rate of traditional search traffic, and 64% of AI-powered sales come from first-time shoppers according to Rep AI data, suggesting strong conversion advantages for platforms with established user relationships. However, the paradox exists that AI can simultaneously reduce technical switching costs (through instant data migration and format conversion) while increasing psychological switching costs through preference attachment.
**Market power dynamics shift from indexing to intelligence**. Search-based commerce derived competitive advantage from index comprehensiveness, ranking algorithm quality, and distribution deals (default placement). LLM commerce shifts market power sources to model sophistication, proprietary training data, user interaction history accumulation, and computational resources for model training and inference. This creates fundamentally different barriers to entry—crawling infrastructure versus GPU clusters, SEO optimization versus Generative Engine Optimization (GEO), and link-based ads versus integrated recommendations.
The network effects differ critically. Traditional search showed limited network effects (better search doesn’t necessarily require more users), enabling long-term market stability with Google maintaining 90%+ share for 25+ years. LLM commerce demonstrates strong network effects where more users generate more query data, enabling better recommendations, attracting more users in a virtuous cycle. However, the competitive dynamics appear more contestable: OpenAI’s market share among AI chatbots declined from 50% in 2023 to 25% in 2025 as Anthropic captured 32%, demonstrating that even with network effects, technological leapfrogging remains possible.
## Two-sided platform pricing theory provides the analytical framework
Standard economic theory of two-sided markets, developed by Rochet and Tirole and extended by Weyl, provides the essential framework for analyzing optimal pricing strategy for OpenAI and predicting commission incidence.
**The fundamental insight of two-sided platform theory** is that platforms must “get both sides of the market on board” by balancing price allocation between users through internalization of indirect network effects. The structure of prices—how total platform fees are divided between buyers and sellers—matters as much as the level of fees. Platforms rationally subsidize one side of the market (even below-cost) to attract participation that creates value for the other side.
In LLM commerce, this manifests as **zero fees to consumers while charging merchants approximately 2%**. This allocation reflects several economic factors. First, consumers have substantially lower search costs in the AI era—they can easily try ChatGPT, Perplexity, Google Gemini, or traditional search without switching costs. Second, consumers don’t internalize the value they create for merchants through their attention and purchase intent. Third, merchants have fewer credible alternatives for reaching 700 million weekly ChatGPT users at high-intent shopping moments. Fourth, network externalities run primarily from consumers to merchants (more shoppers attract merchants) rather than vice versa.
**Weyl’s (2010) framework on monopoly pricing for networks** illuminates why OpenAI prices at approximately 2% despite market power. Profit maximization requires internalizing network externalities to marginal users, not average users. The marginal merchant considering integration weighs OpenAI’s 2% commission against alternatives: Amazon’s 15% plus advertising costs, Google Shopping’s 0% commission but higher customer acquisition cost through ads, TikTok Shop’s 6%, or direct channels. OpenAI must price to attract the marginal merchant who has credible alternatives, even if inframarginal merchants (those without good alternatives) would pay more.
The empirical content of platform pricing models depends critically on user heterogeneity, not just aggregate demand elasticities. Merchants differ substantially in their valuation of ChatGPT distribution: a DTC brand with weak Amazon presence values new discovery channels highly, while established Amazon sellers with optimized FBA operations may view OpenAI as supplementary. OpenAI’s commission structure cannot easily discriminate among merchant types (beyond category-based variation), limiting its ability to extract differential willingness to pay.
**Recent empirical work demonstrates that commission fee regulation often backfires** through unintended platform responses. Li and Wang (2021) studied commission fee caps in 14 U.S. cities for food delivery platforms and found that independent restaurants paying reduced fees experienced declining orders and revenue, while chain restaurants paying original fees saw increased orders. The mechanism: platforms discriminated against independent restaurants post-regulation by reducing recommendations and increasing consumer delivery fees in regulated cities.
This finding has direct implications for LLM commerce. Platforms possess multiple levers beyond commission rates: product ranking, recommendation frequency, prominence in results, consumer-side pricing, and service quality allocation. If OpenAI faces pressure to reduce or cap commission rates, economic theory and empirical evidence suggest it would adjust these other dimensions, potentially harming precisely the merchants that fee caps aimed to help.
**Tremblay (2023) shows that greater fee discrimination allows platforms to serve more markets but increases aggregate double marginalization**, creating a welfare tradeoff where platform profits increase with fee differentiation but social welfare decreases. Yu (2024) demonstrates that commission fee changes affect not just prices but also quality and innovation—welfare analysis ignoring these quality adjustments underestimates policy impacts by 0.91-2.06 percentage points.
The implication for OpenAI’s optimal pricing strategy: uniform low commissions (approximately 2%) may be preferable to complex discriminatory pricing, both for merchant adoption (simplicity, fairness perception) and regulatory risk mitigation. The company’s explicit stance against paid placement reinforces this strategy, positioning commissions as a neutral tax on transactions rather than pay-for-performance positioning.
## Tax incidence theory predicts burden distribution across market participants
Standard tax incidence theory, adapted to two-sided platform markets, predicts where the economic burden of OpenAI’s commission fees will fall. The critical insight is that **statutory incidence (who legally pays) diverges from economic incidence (who bears the burden)**, with distribution determined by relative elasticities, market structure, and pass-through mechanisms.
In a two-sided platform context, a commission rate τ imposed on merchants creates three potential incidence pathways: merchants absorb through reduced margins, consumers bear through higher prices, or the burden falls on displaced competitors (Google, Amazon) through lost market share and pricing power.
**The elasticity principle determines primary incidence**. The side of the market with more inelastic demand or supply bears greater burden. Consumers face relatively elastic demand for most products due to extensive substitutability in e-commerce—if prices rise, they can switch products, brands, or platforms. However, LLM commerce may reduce price elasticity through several mechanisms: curated results show fewer options; AI recommendations frame price within context of fit and quality rather than isolation; trust in recommendations reduces comparison shopping; convenience value raises willingness to pay.
Merchants face potentially inelastic demand for platform access if they lack good alternatives. For merchants currently underserved by Amazon or Google Shopping (artisan creators, niche products, emerging DTC brands), ChatGPT represents non-marginal new distribution opportunity. These merchants can’t easily substitute away from OpenAI without losing access to 700 million weekly users. However, established merchants with optimized Amazon/Google presence face more elastic platform demand—they can reduce ChatGPT focus if commissions or effective costs rise.
**Pass-through rates vary systematically by merchant characteristics and product category**. Economic theory and empirical evidence show that pass-through from cost increases to consumer prices depends on demand curvature (not just elasticity), market structure, product substitutability, and merchant cost structures.
In LLM commerce, pass-through mechanisms operate through multiple stages: Platform (OpenAI) charges commission τ → Merchant faces increased cost per transaction → Merchant decides whether to raise prices, absorb cost, or reduce quality → Consumer responds to any price increase by adjusting quantity demanded or switching to alternatives.
The Congressional Research Service analysis of Digital Services Taxes provides relevant guidance. DSTs function as excise taxes on intermediate services, with burden typically falling on purchasers of digital services and downstream consumers through price increases. The Tax Foundation’s empirical evidence across Austria, France, and UK shows that platforms commonly pass costs to advertisers and merchants, who then pass to consumers—DSTs function as consumption taxes in practice.
Critical insight: **when platforms have market power and merchants face competition, platforms can successfully pass through cost increases**. OpenAI possesses market power from model quality, user base, and network effects. Merchants face intense competition within product categories on the platform. This asymmetry enables OpenAI to maintain commission rates while merchants absorb costs or attempt partial pass-through to consumers.
However, the current low 2% commission rate significantly limits pass-through potential. For a $100 product, the $2 commission represents 2% of transaction value. For a merchant operating on 20% gross margins, this represents 10% of margin. If the merchant attempts to pass through the full commission, the consumer would see a 2% price increase. Given typical e-commerce price elasticities (often greater than 1 for discretionary goods), a 2% price increase would reduce quantity demanded by more than 2%, making pass-through profit-reducing for most merchants.
**The more likely incidence outcome**: merchants absorb most of the 2% commission through reduced margins, with minimal pass-through to consumers. This burden distribution would differ markedly from the counterfactual of higher commission rates (10-15% like traditional platforms), which would force substantial pass-through or merchant exit.
**Incidence on displaced competitors represents the third pathway**. If LLM commerce captures market share from Google and Amazon, the burden manifests as reduced revenues, lower advertising pricing power, and loss of data network effects for these incumbents. This is not traditional tax incidence but rather incidence through competitive displacement.
The magnitude of this effect depends on substitutability between channels. Current evidence suggests complementary rather than purely substitutional relationships: users employ LLM commerce for discovery and consideration while still completing transactions on familiar platforms. Adobe data showing 9% lower conversion rates for AI traffic but 8% higher engagement suggests LLM commerce currently directs traffic to traditional platforms rather than capturing the full transaction.
However, as Instant Checkout adoption increases and user trust develops, the substitution effect will strengthen. Google faces particular vulnerability: its elimination of shopping commissions to compete with Amazon demonstrates declining pricing power. If ChatGPT captures product discovery at 2% commissions while Google can’t monetize through commissions at all, the incidence manifests as Google’s lost advertising revenue and market power erosion.
Amazon faces different dynamics. Its high commissions (15% referral fees plus FBA plus advertising) create margin for competitive entry, but its conversion rates, fulfillment infrastructure, and Prime ecosystem provide substantial moat. The incidence question becomes: do Amazon merchants view ChatGPT as additive distribution (expanding total sales) or substitutional (shifting sales between channels)? If additive, Amazon bears minimal burden. If substitutional, Amazon sees commission revenue per merchant decline as sales shift to lower-rate channels.
**Network effects amplify incidence impacts through dynamic adjustments**. When consumers shift attention to ChatGPT for shopping, this reduces the value of Amazon and Google to merchants (weakening indirect network effects), potentially forcing these platforms to reduce fees to retain participation. When merchants integrate with ChatGPT, this increases platform value to consumers, strengthening OpenAI’s network effects and pricing power.
Birge et al. (2019) demonstrate that platforms should charge different commissions to different trader types based on network positions, even when traders have identical value distributions. Revenue loss can be unbounded when charging uniform fees. This suggests OpenAI’s current uniform 2% commission may be suboptimal in the long run—differentiated pricing by merchant or category could extract more surplus as the platform matures.
Even-Tov et al. (2022) show that retail investors are highly responsive to commission fee changes, with fee elimination leading to 30% increases in trading frequency. The retail trading context provides analogy: when commissions represent small absolute amounts but meaningful percentages of transaction sizes, users demonstrate high elasticity. This supports the prediction that merchants and consumers will be highly responsive to LLM commerce commission rates, justifying OpenAI’s low-rate market entry strategy.
## Market power considerations determine long-run pricing and incidence
The long-run incidence of LLM commerce fees depends critically on whether OpenAI and competitors maintain pricing discipline or engage in fee competition, which in turn depends on market structure, barriers to entry, and competitive dynamics.
**OpenAI currently possesses substantial market power** from several sources: ChatGPT holds 80.1% of AI-driven search market share; 700 million weekly active users create reach advantages; proprietary GPT-4 and successor models demonstrate technological leadership; accumulated conversation data enables superior personalization; and brand recognition in conversational AI exceeds competitors.
However, this market power faces important constraints. **Barriers to entry are high but not prohibitive**. Anthropic captured 32% of enterprise LLM market share by 2025 (surpassing OpenAI’s 25%), demonstrating that technological leapfrogging remains possible. Open-source models like Meta’s Llama (9% enterprise share) prevent complete platform closure. Google possesses distribution advantages through Chrome, Android, and Search integration. Amazon has Rufus shopping assistant integrated with its marketplace.
The FTC analysis identifies control over AI’s essential building blocks—training data, specialized talent, computational resources, and platform effects—as sources of market power distinct from traditional search. Data network effects create barriers as platforms with more user interactions improve recommendations faster, attracting more users. OpenAI’s 700 million weekly users generate interaction data that competitors must overcome through superior model capabilities or alternative data sources.
**Switching costs cut both ways in determining market power sustainability**. Personalization creates psychological attachment and bilateral learning lock-in, supporting platform pricing power. But low monetary switching costs (AI platforms are free to users) and AI-assisted data migration reduce technical barriers to switching, maintaining contestability.
The Brookings Institution research on firm-level AI adoption (2010-2018) shows that AI investments increase industry concentration, with largest firms benefiting disproportionately through data advantages and computing resources. The effect creates winner-take-more dynamics, but the research also notes rapid technological evolution prevents permanent dominance.
**The landmark Google antitrust case provides crucial precedent** for understanding competitive dynamics. Judge Mehta’s September 2025 ruling noted that “the emergence of GenAI changed the course of this case” and declined to order Chrome divestiture because AI competition was deemed “highly competitive” with “numerous new market entrants.” ChatGPT was mentioned 28 times, OpenAI 30 times, demonstrating judicial recognition that LLM platforms created competitive constraints on Google’s search monopoly.
The American Action Forum analysis emphasizes that market power in AI is “transitory” because “ChatGPT hit 1 billion searches/day in less than 2 years; Google needed 11 years.” This suggests that even substantial market share doesn’t guarantee sustained pricing power when technological substitution occurs rapidly.
**Multi-homing behavior by merchants reduces platform market power**. If merchants easily integrate with ChatGPT, Google Shopping, Amazon, and TikTok Shop simultaneously, platforms must compete for merchant attention and consumer recommendations. The Agentic Commerce Protocol’s open architecture facilitates multi-homing by reducing integration costs. However, if consumers consolidate shopping queries on a single LLM platform due to personalization quality, this gives the dominant platform substantial leverage over merchants despite multi-homing capability.
Song (2021) demonstrates that platform mergers may benefit some agents by lowering prices or attracting more users on the other side through indirect network externalities, showing that market concentration doesn’t necessarily harm all participants. In LLM commerce, if OpenAI gains dominance, merchants might benefit from reduced integration complexity (single platform rather than many), but lose bargaining power over commission rates.
**The critical policy levers for maintaining competitive pricing discipline** are data access and interoperability. The Brookings, FTC, and NBER analyses converge on the insight that data market operation determines whether market power emerges and persists. If shopping interaction data, merchant product feeds, and user preference information can flow across platforms, competition remains viable. If proprietary data creates insurmountable moats, winner-take-all dynamics enable monopoly pricing.
OpenAI’s open-sourcing of the Agentic Commerce Protocol suggests recognition that standards-based interoperability supports rapid merchant adoption and reduces regulatory scrutiny, even if it constrains long-run platform lock-in. Stripe’s co-development ensures payment infrastructure remains open across platforms, preventing vertical foreclosure concerns.
**Recommendation bias emerges as the key market failure concern**. Unlike search advertising where paid placement is clearly marked, LLM recommendations blend algorithmic judgment with potential commercial influence. If OpenAI can bias recommendations toward higher-commission merchants or partners while maintaining plausible deniability (“our AI determined this was the best fit”), it gains ability to extract surplus through implicit rather than explicit pricing mechanisms.
The FTC’s emphasis on algorithmic transparency and prohibition of exclusionary conduct becomes critical. If platforms face credible regulatory constraints on recommendation bias, commission rates remain the primary revenue extraction mechanism. If platforms can subtly favor commercial partners in recommendations, explicit commission rates understate true merchant costs of platform access.
Lin, Pan, and Zheng (2020) show that platform pricing interacts with strategic buyer behavior and cost expectations in complex ways that can contradict conventional wisdom. Applying this to LLM commerce: merchants’ willingness to pay commissions depends on expectations of platform growth, consumer adoption trajectory, and competitor behavior. OpenAI’s initial low 2% rate may be strategically optimal even with market power if it accelerates merchant adoption and consumer trust, enabling higher rates later as merchants develop platform dependency.
## Optimal pricing strategy balances adoption, extraction, and competitive response
Synthesizing the theoretical frameworks, empirical evidence, and market structure analysis yields predictions for OpenAI’s optimal pricing strategy and likely commission incidence distribution.
**For OpenAI’s optimal commission rate**, the 2% level appears strategically sound for market entry and early growth phase, justified by:
**Adoption economics**: Low rates reduce merchant integration friction and eliminate price objections, enabling rapid scaling to critical mass of product coverage. Network effects mean platform value is convex in merchant participation—reaching 1 million Shopify merchants creates substantially more than 10x the value of 100,000 merchants. Sacrificing short-term commission revenue for faster network growth likely maximizes long-term platform value.
**Competitive positioning**: The rate undercuts all major competitors (Amazon 15%, Google Shopping’s historical 12%, TikTok Shop 6-8%), creating clear value proposition for merchants. It’s low enough to appear insignificant to merchants operating on 20-30% margins, reducing scrutiny and negotiation friction. The rate positions ChatGPT as additive distribution channel rather than Amazon alternative, reducing competitive retaliation risk.
**Regulatory strategy**: A “small fee” below typical platform rates reduces antitrust scrutiny. The explicit prohibition on paid placement and rank manipulation preempts pay-to-play concerns. Open-sourcing the Agentic Commerce Protocol signals non-proprietary intentions. Low rates make merchant complaints less likely, reducing political pressure for regulation.
**Elasticity calibration**: At 2%, commission represents small enough percentage of transaction value that merchant pass-through is profit-reducing for most products given typical demand elasticities. This means OpenAI captures revenue while minimizing consumer price distortion and deadweight loss.
However, the long-run optimal rate likely exceeds 2% if OpenAI achieves sustainable competitive advantages. The platform could increase commissions to 4-6% (matching TikTok Shop range) once merchant dependency develops through: (1) proprietary user data accumulated through shopping interactions; (2) GEO optimization by merchants specifically for ChatGPT; (3) consumer habit formation around ChatGPT shopping; (4) branded product discovery (“find it on ChatGPT” as consumer behavior).
The timing and magnitude of rate increases will depend on competitive dynamics. If Perplexity, Google Gemini, or other LLM platforms offer shopping at 0-1% commissions, OpenAI faces constraint. If competitors adopt similar 2% rates, industry norm develops enabling eventual increases.
**Alternative revenue models may dominate commission extraction**. Sam Altman’s comments suggest preference for subscription tiers with shopping benefits over advertising, and skepticism toward traditional ads. OpenAI could extract surplus through: Premium memberships with better shopping features (perhaps commission-free purchases for Pro subscribers); data licensing to merchants (aggregated shopping insights); SaaS fees for merchant tools and analytics; API fees for third-party shopping agents built on ChatGPT; advertising with strict quality constraints (“tasteful ads” that maintain user trust).
The optimal long-run model likely combines low commissions (maintaining participation) with premium subscription value (extracting consumer surplus) and merchant services (extracting merchant surplus through non-commission channels). This multi-sided revenue approach reduces reliance on any single mechanism and aligns incentives across participants.
**For commission incidence distribution**, the theoretical and empirical analysis predicts:
**Short-run (2% commission rate)**: Merchants absorb 70-90% of commission burden through reduced margins; consumers bear 5-15% through modest price increases on some products; displaced competitors (Google, Amazon) absorb 5-25% through lost market share and reduced pricing power. Minimal deadweight loss due to low rate and high elasticities.
**Long-run (4-6% commission rate, if achieved)**: Merchants absorb 40-60% of commission burden; consumers bear 20-40% through noticeable price increases; displaced competitors absorb 10-30% through sustained market share loss. Moderate deadweight loss as price increases reduce transaction volumes; quality adjustments as merchants optimize for commissions rather than pure fit.
**The distribution depends critically on elasticities that remain uncertain**: Consumer price elasticity in LLM commerce after trust develops; merchant supply elasticity to ChatGPT vs. alternative platforms; cross-platform elasticity (multi-homing ease); competitive response elasticity from Google and Amazon.
**Displaced competitors face structural incidence through several mechanisms**: Google’s loss of product discovery queries reduces advertising inventory value; Amazon’s loss of product search mind share weakens marketplace network effects; traditional affiliate networks face disintermediation as LLM platforms offer superior discovery; comparison shopping engines lose relevance as AI provides synthesis rather than requiring user comparison.
The magnitude of competitive displacement incidence depends on whether LLM commerce proves complementary (expanding total e-commerce) or substitutional (redistributing existing commerce). Current evidence suggests hybrid: LLM platforms expand discovery and consideration stages (complementary) while potentially capturing transaction completion (substitutional).
**Policy implications for tax incidence analysis**: Regulators concerned about commission burden on small merchants should focus on data access policies and interoperability requirements rather than rate caps. Empirical evidence shows fee caps often backfire through platform adjustments on other dimensions. Ensuring merchants can multi-home across platforms maintains competitive constraint on commission rates more effectively than direct regulation.
Transparency requirements around algorithmic ranking and recommendation logic help prevent implicit burden increases through biased recommendations. If platforms must disclose how commercial factors influence suggestions, merchants can evaluate true costs of platform participation beyond nominal commission rates.
Dynamic efficiency considerations matter: short-run burden on merchants from modest commissions may be offset by long-run consumer welfare gains from improved discovery and reduced search costs. Static incidence analysis that ignores quality improvements and search cost reductions will overestimate net burden.
## Emerging market structure remains contested and shapeable
LLM-mediated commerce is fundamentally reshaping digital commerce economics, but the ultimate market structure, pricing dynamics, and incidence distribution remain uncertain and policy-shapeable.
**The theoretical frameworks from two-sided platform economics, tax incidence theory, and industrial organization provide robust analytical tools**, but their application requires careful attention to the specific characteristics of LLM commerce: high initial accuracy uncertainty affecting trust and adoption; bilateral learning creating switching costs; recommendation synthesis creating information asymmetries; network effects from user interaction data; rapid technological evolution enabling competitive disruption.
**OpenAI’s approximately 2% commission represents strategically sound market entry pricing** that balances merchant adoption incentives, competitive positioning, regulatory risk mitigation, and consumer welfare effects. The incidence at this rate falls primarily on merchants through reduced margins, with minimal consumer price impacts and modest competitive displacement of Google and Amazon’s market power.
**As the market matures, commission rates will adjust based on competitive dynamics, regulatory constraints, and merchant bargaining power**. If OpenAI achieves sustained competitive advantages through data accumulation and user lock-in, rates could increase to 4-6% range. If intense competition from Google, Perplexity, and other platforms persists, rates may remain constrained at 2% or below.
**The critical policy levers are data access frameworks and interoperability standards** rather than direct commission rate regulation. Ensuring merchants and consumers can multi-home across platforms maintains competitive constraint on pricing. Algorithmic transparency prevents implicit burden increases through recommendation bias.
**Tax incidence theory predicts that burden distribution depends on relative elasticities**, which remain uncertain given LLM commerce’s novelty. As consumer trust develops and LLM shopping becomes habitual, demand may become less elastic, enabling higher rates with greater pass-through to consumers. As merchants optimize specifically for ChatGPT presence (GEO, product feed optimization), supply becomes less elastic, enabling platforms to extract more surplus.
**The displacement of Google and Amazon represents a form of incidence through competitive erosion** that may ultimately exceed the direct burden of commissions on merchants and consumers. If LLM platforms capture product discovery, the value of search advertising and marketplace prominence declines, reducing incumbent pricing power. This mechanism redistributes surplus from established intermediaries to new LLM platforms without necessarily increasing total intermediation costs.
The race to establish standards, accumulate user trust, and achieve network scale will determine long-run market structure. OpenAI’s aggressive pricing, open protocol strategy, and prohibition on paid placement suggest an attempt to become the dominant trusted platform before competitors establish positions. Whether this succeeds depends on execution, competitive response, and regulatory evolution. The economic analysis indicates that modest commissions on high-quality recommendations could create substantial platform value while maintaining reasonable burden distribution across participants—but only if competitive constraints and algorithmic transparency prevent extraction of excessive surplus.
TC again: But then again, I guess most of you could do better than that!
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2025-09-30 04:19:12
“Sonnet 4.5 does complete replication checks of an econpaper.”
That is Kevin Bryan, here is more from Ethan Mollick.
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