a16z Analyst Tugce Erten: How AI Applications Maintain Premiums as Model Costs Decline
a16z analyst Tugce Erten proposes a three-layer framework explaining how AI application companies can resist replacement by general-purpose models: executing workflows better than customers, making context rental cheaper than building in-house, and pricing by identifiable work units. The article uses the blind test case of legal AI company Harvey at Spanish energy firm Repsol, the adoption case of customer service AI company Decagon at Notion, and the pricing breakdown of data company Clay to illustrate the sources and boundaries of application-layer premiums.
As inference costs continue to decline, they present both an opportunity and a threat for AI application companies. In this article published on November 2, 2026, a16z analyst Tugce Erten offers a core judgment: cheaper inference can improve the profit margins of application companies, but more capable models and easier development tools will also make certain parts of applications easier to replace. The commercial question thus becomes: as customers' alternatives continue to improve, what will they continue to pay for?
Erten’s criterion for testing this is straightforward: an application is in a vulnerable state when a customer can reproduce its core output using a general-purpose model with only minimal ongoing effort. A buyer from a global professional services firm told us that if sending a direct prompt to a frontier model produced better work products than a vendor’s tool, the firm saw little reason to pay a platform premium. The firm had even built an internal AI platform for sensitive projects, capable of running models locally or in private environments, and was experimenting with open-source code and alternatives to commercial applications.
Based on these observations, Erten distills three rules. The first is to execute workflows better than the customer can. Customers do not need to rebuild the entire application to weaken your pricing power; moving one high-volume task in-house or establishing a credible alternative is often enough. Take contract review as an example: this means the application company must enforce the client company’s legal standards, maintain consistency across edits in documents, and preserve context as negotiations evolve. The multi-agent review process of legal AI company Harvey demonstrates this depth of execution: a lead agent distributes reviews among specialized agents, coordinates conflicting edits, checks the final document, selects models based on quality, speed, and cost for each task, and preserves review context so subsequent requests can build upon previous work. Harvey also describes opportunities to learn from redlines that lawyers accept, edit, or reject, thereby requiring less guidance for future reviews.
In a blind test involving Spanish energy company Repsol, the legal team compared Harvey against Microsoft Copilot and its own internal prototype, Lexia. Harvey delivered the highest-quality output in the blind test, and subsequently outperformed the alternatives in accuracy and time savings during an eight-week pilot involving 50 lawyers across 12 jurisdictions. Repsol then proceeded with a full deployment. Harvey reports that lawyers now save four to six hours per week, with adoption reaching 96% within the legal department. Erten points out that this advantage compounds with use: every completed workflow strengthens evaluation, validates context, and deepens customer-specific knowledge, allowing the application’s advantage to accumulate as learning improves subsequent results.
The second rule is to make renting context cheaper than building it yourself. Context can justify a premium through exclusive access or useful preparation. Erten cites an example of a vertical software company: having accumulated proprietary pricing benchmark data for approximately 3,000 companies, it is exploring allowing AI systems to query directly via a Model Context Protocol (MCP) server. The economic logic is clear: the vendor collects, organizes, verifies, and maintains information once, then spreads the cost across many customers; each customer rents reliable, up-to-date context at a lower cost than independently reconstructing and maintaining equivalent resources. The same logic may apply to customer-owned information—policies, CRM records, contracts, and workflow history may already belong to the buyer, but preparing them for reliable use requires permissions, standardization, retrieval, and continuous verification.
The third rule is to prove value, then price to capture it. Erten references a survey conducted by a16z in August 2026 of 100 enterprise large language model decision-makers: 94% expected total LLM spending to increase, and 37% expected it to at least double within the next year. As spending grows, customers need to see what it delivers: how much work is completed, at what quality, and at what total cost relative to alternatives. Pricing should start with work units recognizable to the customer—a reviewed contract, a resolved customer service conversation, a completed research task—and measure the full delivery cost, including retrieval, model calls, tools, retries, evaluation, and human review, calculating the cost per successfully completed unit while including failed attempts.
Regarding pricing structures, Erten specifically highlights the approach of data company Clay. Clay’s pricing memo described how fixed AI prices overcharged simple workloads while making complex ones unprofitable. Clay’s response was to separate Data Credits from orchestration actions, retaining fixed credit pricing for more bounded AI workloads, and passing through actual token costs for more variable reasoning models without any markup. This gives customers a way to pay for orchestration while treating volatile model inputs separately. When prices are tied to identifiable work, vendors can use routing, caching, or better models to improve margins, reduce prices, or include more work, allowing customers to share in these benefits without tracking every change in the underlying model mix.
Erten ultimately reduces the test to one sentence: does buying your application offer a better combination of total cost, quality, speed, and risk than the customer’s best credible alternative? This comparison includes your price as well as the effort required to implement, operate, and maintain each option. She simultaneously leaves a boundary condition: as model progress continually improves customers’ alternatives, the application’s advantage must widen faster to continue earning a premium.
It is worth noting that all performance data cited in the article comes from self-reported vendor figures. Harvey’s numbers regarding time savings and adoption rates, as well as Decagon’s report of a 34% improvement in Notion ticket resolution times, are case materials released by the companies themselves, rather than results verified independently. Erten’s framework itself is an attribution argument; it explains how application companies can build and demonstrate premiums, but it does not constitute verification that these premiums have been independently confirmed by the market.