token usage
token usage on Beyond Market Intelligence: a running collection of 6 stories we have gathered and hand-picked because they are worth your time. Every post here touches on token usage in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around token usage, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Runable hits $21M to bet AI agents can go from building businesses to growing them
Runable, a platform focused on empowering AI agents to manage and scale businesses, has secured $21 million in funding. The company’s core proposition is enabling users to move beyond initial business building and into sustained growth through AI. Notably, Runable reports that 60%–70% of its substantial token usage—over 1 trillion tokens in the last 90 days—originates from paying customers, demonstrating early market traction.
One in five enterprises can't stop a runaway AI agent's spending in real time
Enterprise adoption of AI agents is revealing a critical shift: organizations are increasingly deploying multiple orchestration platforms—averaging three—to mitigate vendor risk and retain control. This trend, driven by concerns around security, permissions, and visibility, sees Microsoft AI Foundry/Copilot Studio leading usage, with Anthropic's Claude Platform gaining significant consideration. Notably, one in five enterprises still lacks real-time control over agent spending, highlighting the need for robust oversight as AI deployments evolve. Learn more about this emerging landscape with VentureBeat's coverage of Serval’s AI agent, Catalyst.
How to Build a Simple AI Web Scraper with Python
Unlock the power of any webpage with a simple AI web scraper built using Python. This guide demonstrates how to transform ordinary websites into lightweight, LLM-powered QA engines. By efficiently cleaning HTML, converting content to Markdown, and refining prompts, you can extract focused answers while minimizing token usage. It’s an accessible entry point to agentic AI—much like the exploration of AI agents discussed in "5 Fun Agentic AI Papers to Read." Discover a practical approach to harnessing AI for targeted data extraction and insightful question-answering.
Presentation: The Five Stages of AI Maturity in Engineering Organizations - Where and Why Teams Get Stuck
Soaring AI spending isn’t automatically translating to improved software delivery—a critical challenge for engineering leaders. Quotient CEO Lizzie Matusov unpacks why, presenting a research-backed AI maturity framework to move beyond superficial metrics and unlock measurable business outcomes. This presentation identifies five key stages of AI adoption, highlighting common bottlenecks across the software development lifecycle and offering actionable strategies for advancement.
A Guide to Saving Token Usage with Multi-Agent AI
Scaling multi-agent AI can unlock incredible potential, but escalating costs are a common concern. This guide outlines four key strategies to optimize token usage and ensure efficient scaling. Learn how to streamline your architecture without sacrificing performance, enabling you to explore increasingly complex AI applications. We’ll equip you with practical techniques to maximize your investment and drive tangible results. For a deeper dive into agent architecture and real-world API performance, see our article, "Does MiniMax Agent Actually Make Work Easier?".
Prompt Compression Techniques: How to Reduce LLM Costs Without Losing Important Context
Large language models frequently process more information than necessary, driving up costs and potentially obscuring crucial details. Prompt compression techniques offer a solution, reducing prompt size while preserving essential meaning and instructions. This allows for more efficient token usage, faster response times, and improved clarity for the model. Explore strategies to streamline your prompts and optimize performance—discover how to transform your LLM interactions for greater efficiency. For a deeper dive into related challenges, see "AI agents aren't confidently wrong because of bad context."