Y CombinatorAaron Levie: Why Enterprise Buys Outcomes, Not Models
By treating intelligence as a commodity layer like storage or compute; B2B AI startups capture Jevons paradox gains as enterprise adoption stays under 1%.
At a glance
WHAT IT’S REALLY ABOUT
Enterprise AI Revolution: From Commodity Intelligence To Explosive Software Growth
- Aaron Levie of Box joins YC partners to discuss how AI is transforming enterprise software, arguing that intelligence is becoming a cheap, fungible commodity while value shifts to the workflows, integrations, and outcomes built on top of models.
- He explains that enterprises do not want models per se; they want end-to-end outcomes that plug into existing systems, with model choice increasingly abstracted away and interchangeable as costs converge toward the price of raw compute.
- Levie draws parallels to the cloud transition, predicting that AI will massively expand software TAM by enabling companies to automate work they never did before, rather than simply replacing existing labor budgets.
- He anticipates an AI-driven abundance economy—if regulatory and societal choices cooperate—where productivity gains are reinvested into better products, more competition, and ultimately improved services and lower costs for consumers.
IDEAS WORTH REMEMBERING
5 ideasDon’t build ‘just a wrapper’—own a full workflow and outcome.
Levie argues that real value in B2B AI lies in the software around the model—workflows, integrations, business logic, and proprietary customer data—so startups should solve end-to-end problems rather than thinly exposing model outputs that platforms can absorb.
Treat models as interchangeable infrastructure; design for abstraction.
Enterprises increasingly care about outcomes, SLAs, security, and integrations more than which model is used; successful AI apps will be architected so models can be swapped as prices fall and capabilities converge, improving margins over time.
Pure-play model companies are a bad standalone business bet.
With OpenAI, Anthropic, hyperscalers, and open source (Meta, DeepSeek) driving prices down, Levie believes model-only businesses without a substantial software layer, distribution, or ecosystem lock-in will be squeezed by open models and cheaper incumbents.
Most enterprises will buy AI for ‘context’ and build AI for ‘core.’
Using Geoff Moore’s ‘core vs context’ frame, Levie says companies should buy AI-powered systems for generic functions (HR, ERP, CRM) but build proprietary AI where it directly differentiates their offering, such as trading algorithms, drug discovery, or personalization.
AI will expand software markets by enabling entirely new work, not just replacing labor.
Many AI use cases (e.g., automatic contract data extraction, mass localization, lead generation) were previously not done at all, so spend is additive; Levie predicts software/AI TAM could grow 5–10x as firms pay for work previously uneconomical to perform.
WORDS WORTH SAVING
5 quotesAn enterprise doesn’t want a model. It wants an outcome.
— Aaron Levie
We used to have an API into compute and storage. Now we have an API into intelligence.
— Aaron Levie
The cost of intelligence is going to go to zero. It’s absolutely guaranteed.
— Aaron Levie
Most of the way AI will show up to a knowledge worker in 2030 will be from what we would have thought of as an ISV ten years prior.
— Aaron Levie
The revolution does not have to be Black Mirror. It could be something that is driven by Jevons paradox, driven by abundance for everyone.
— Garry Tan (closing reflection on Levie’s vision)
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