Ascend '26 · workshop
Tokenomics
Are you paying too much for AI?
AI is changing how leaders must think about the true cost of technology with rapid competition among foundation models, shifting API pricing and debates around open-weight vs. proprietary architectures. Determining your actual ROI can feel like trying to hit a moving target.
Join Nick Jenkins, AI Specialist at Google Cloud, for a pragmatic and hype-free breakdown of the economics of AI. Discover how to cut through the noise, optimise model spending and build a sustainable AI strategy. In this session we’ll demystify Tokenomics and move beyond simple token pricing to focus on the metric that matters most: Cost Per Task Achieved.
Whether you’re evaluating frontier models, deploying open-weight solutions or looking to cut model expenditure by up to 80% with smart optimisation strategies, this session will provide you with a concrete framework to plan, budget and confidently scale AI across your organisation.
In partnership with
What to expect
- 📐 The core metric: Why Cost Per Task Achieved matters more than raw token pricing.
- ☁️ Deployment trade-offs: Comparing public APIs, cloud-hosted open-weights and self-hosted models.
- 💰 Smart cost optimisation: Slash spend by up to 80% using model ensembling, context caching and prompt engineering.
- 🚧 Strategic guardrails: Setting FinOps limits, knowing when traditional ML beats GenAI and ethical considerations.