Pricing and Competition for Generative AI

• URL: http://arxiv.org/abs/2411.02661v1
• Date: Mon, 04 Nov 2024 22:52:45 GMT
• Title: Pricing and Competition for Generative AI
• Authors: Rafid Mahmood,
• Abstract summary: We explore the problem of how developers of new generative AI software can release and price their technology. We first develop a comparison of two different models for a specific task with respect to user cost-effectiveness. We then model the pricing problem of generative AI software as a game between two different companies.
• Score: 3.8677478583601776
• License:
• Abstract: Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii) users interact with this model over a series of natural language prompts; and (iii) the model is ideally evaluated on binary user satisfaction with respect to model outputs. Given these characteristics, we explore the problem of how developers of new generative AI software can release and price their technology. We first develop a comparison of two different models for a specific task with respect to user cost-effectiveness. We then model the pricing problem of generative AI software as a game between two different companies who sequentially release their models before users choose their preferred model for each task. Here, the price optimization problem becomes piecewise continuous where the companies must choose a subset of the tasks on which to be cost-effective and forgo revenue for the remaining tasks. In particular, we reveal the value of market information by showing that a company who deploys later after knowing their competitor's price can always secure cost-effectiveness on at least one task, whereas the company who is the first-to-market must price their model in a way that incentivizes higher prices from the latecomer in order to gain revenue. Most importantly, we find that if the different tasks are sufficiently similar, the first-to-market model may become cost-ineffective on all tasks regardless of how this technology is priced.

Related papers

• LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication [11.337245234301857]
  We argue that a pay-as-you-go service mode will be suitable in this context, referred to as Large Model as a Service (LM) We propose an Iterative Model Pricing (IMP) algorithm that optimize the prices of large models iteratively by reasoning customers' future rental decisions. In the second step, we optimize customers' selection decisions by designing a robust selecting and renting algorithm.
  arXiv  Detail & Related papers  (2024-01-05T07:19:19Z)
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)
• Modeling Choice via Self-Attention [8.394221523847325]
  We show that our attention-based choice model is a low-optimal generalization of the Halo Multinomial Logit (Halo-MNL) model. We also establish the first realistic-scale benchmark for choice estimation on real data, conducting an evaluation of existing models.
  arXiv  Detail & Related papers  (2023-11-11T11:13:07Z)
• STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning [82.03481509373037]
  Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. We introduce Transformer-based wORld Model (STORM), an efficient world model architecture that combines strong modeling and generation capabilities. Storm achieves a mean human performance of $126.7%$ on the Atari $100$k benchmark, setting a new record among state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-14T16:42:02Z)
• UnIVAL: Unified Model for Image, Video, Audio and Language Tasks [105.77733287326308]
  UnIVAL model goes beyond two modalities and unifies text, images, video, and audio into a single model. Our model is efficiently pretrained on many tasks, based on task balancing and multimodal curriculum learning. Thanks to the unified model, we propose a novel study on multimodal model merging via weight generalization.
  arXiv  Detail & Related papers  (2023-07-30T09:48:36Z)
• UniMatch: A Unified User-Item Matching Framework for the Multi-purpose Merchant Marketing [27.459774494479227]
  We present a unified user-item matching framework to simultaneously conduct item recommendation and user targeting with just one model. Our framework results in significant performance gains in comparison with the state-of-the-art methods, with greatly reduced cost on computing resources and daily maintenance.
  arXiv  Detail & Related papers  (2023-07-19T13:49:35Z)
• Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs [66.30706841821123]
  Large language models (LLMs) power many state-of-the-art systems in natural language processing. LLMs are extremely computationally expensive, even at inference time. We propose a new metric for comparing inference efficiency across models.
  arXiv  Detail & Related papers  (2023-05-03T21:51:42Z)
• Costs to Consider in Adopting NLP for Your Business [3.608765813727773]
  We show the trade-off between performance gain and the cost across the models to give more insights for AI-pivoting business. We call for more research into low-cost models, especially for under-resourced languages.
  arXiv  Detail & Related papers  (2020-12-16T13:57:31Z)
• Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
  One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy. We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
  arXiv  Detail & Related papers  (2020-07-14T16:42:59Z)
• AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses [97.50616524350123]
  We build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch. The second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level. The third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal.
  arXiv  Detail & Related papers  (2020-01-15T18:32:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.