arXiv Machine Learning
Jul 14
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
arXiv:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.
Introducing our new pricing
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OpenAI Blog
Feb 27
Scaling AI for everyone
Today we’re announcing $110B in new investment at a $730B pre money valuation. This includes $30B from SoftBank, $30B from NVIDIA, and $50B from Amazon.
arXiv Machine Learning
Jun 15
High-Frequency Pricing at Scale for E-Commerce
arXiv:2606. 13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce.
arXiv AI
Aug 12
Pricing Access to Dynamic Information Services
arXiv:2510. 09859v5 Announce Type: replace-cross Abstract: A provider sells a \emph{dynamic information service}---a real-time, capacity-constrained process that resolves a customer's uncertainty---to customers who differ privately in urgency.
arXiv Machine Learning
Jul 28
On Non-Stationary Dynamic Pricing: Adaptivity and Optimality
arXiv:2607. 24115v1 Announce Type: cross Abstract: We study the contextual dynamic pricing problem under non-stationarity, where a firm sells products to $T$ sequentially arriving consumers that behave according to an unknown demand model that can change over time.
Towards Data Science
1d ago
Google Offered $10M for a Dying Airline's Data. How Can You Value Yours?
How to put a dollar value on your organization's operational data The post Google Offered $10M for a Dying Airline's Data. How Can You Value Yours? appeared first on Towards Data Science.
Hugging Face Trending Papers
Aug 13
Keep, Customize, or Exit: Default Design and Token Pricing in LLM Reasoning Services
We study a large language model (LLM) service in which a provider chooses a per-token price and a default reasoning-token allocation, while a user may accept the default, customize the allocation, or exit. Larger allocations can improve accuracy but increase token cost and latency.
arXiv Machine Learning
Jun 8
Learning Fair Demand Models
arXiv:2606. 06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail.
arXiv AI
Jul 9
Memory Scarcity, Open Models, and the Restructuring of the AI Industry, 2026-2030 -- A quantitative scenario analysis of inference economics, training-cost divergence, and infrastructure solvency
arXiv:2607. 07207v1 Announce Type: cross Abstract: We analyze how four forces restructure the AI industry over 2026-2030: the DRAM/HBM price surge, frontier-capable open-weight models (GLM-5.
arXiv AI
Aug 14
Keep, Customize, or Exit: Default Design and Token Pricing in LLM Reasoning Services
arXiv:2608. 13315v1 Announce Type: cross Abstract: We study a large language model (LLM) service in which a provider chooses a per-token price and a default reasoning-token allocation, while a user may accept the default, customize the allocation, or exit.
Simon Willison
Aug 23
Anthropic’s best AI model struggles to attract users as cheaper tools thrive
Anthropic’s top AI model is struggling to attract users even as cheaper alternatives thrive. The company’s July revenue is projected at $65 bn, up from $47 bn in May, and it expects Q3 profitability while boasting 6,000 high‑spending customers. In contrast, OpenAI’s revenue has risen 35 % this quarter, spurred by GPT‑5.6, and a Ramp AI index shows Anthropic’s newer models (e.g., Fable) are less popular than older ones like Opus 4.8.
arXiv Computation and Language
Sep 1
LLP: LLM-Based Product Pricing in E-commerce
The paper introduces LLP, a Large Language Model–based generative framework for pricing second‑hand products on consumer‑to‑consumer platforms. LLP retrieves similar items to capture market dynamics, then uses LLMs to generate price suggestions, refined through supervised fine‑tuning and group relative policy optimization. A confidence‑based filter rejects unreliable predictions, and experiments show LLP outperforms prior methods, achieving higher static adoption rates when deployed on Xianyu.