[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-1c6f383e47085f06-measuring-ai-agents-the-entropy-matrix-and-mousepo-summary":3,"summaries-facets-categories":128,"summary-related-1c6f383e47085f06-measuring-ai-agents-the-entropy-matrix-and-mousepo-summary":7834},{"id":4,"title":5,"ai":6,"body":13,"categories":87,"created_at":89,"date_modified":89,"description":81,"extension":90,"faq":89,"featured":91,"kicker_label":89,"meta":92,"navigation":107,"path":108,"published_at":109,"question":89,"scraped_at":110,"seo":111,"sitemap":112,"source_id":113,"source_name":114,"source_type":115,"source_url":116,"stem":117,"tags":118,"thumbnail_url":123,"tldr":124,"tweet":125,"unknown_tags":126,"__hash__":127},"summaries\u002Fsummaries\u002F1c6f383e47085f06-measuring-ai-agents-the-entropy-matrix-and-mousepo-summary.md","Measuring AI Agents: The Entropy Matrix and 'Mousepower'",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","google\u002Fgemini-3.1-flash-lite",8649,664,3338,0.00315825,{"type":14,"value":15,"toc":80},"minimark",[16,21,25,28,32,35,52,73,77],[17,18,20],"h2",{"id":19},"the-measurement-problem-tokens-vs-outcomes","The Measurement Problem: Tokens vs. Outcomes",[22,23,24],"p",{},"Agents are currently suffering from a \"measurement problem\" where early adopters are biased and token usage is often untethered from business value. The current cycle involves \"token maxing\" into austerity, followed by dropping out, and eventually returning due to FOMO.",[22,26,27],{},"To break this cycle, builders must stop treating tokens as the primary metric. Tokens are an output, not an outcome. Value should be measured by tangible results—such as bugs closed or support tickets resolved—rather than the volume of compute consumed. The current bottleneck in AI-assisted workflows is often human verification; while agents can generate code or designs rapidly, the human review process has not scaled, making it difficult to judge the quality of the output at the speed of production.",[17,29,31],{"id":30},"the-entropy-matrix-a-framework-for-task-selection","The Entropy Matrix: A Framework for Task Selection",[22,33,34],{},"To determine which tasks are suitable for agents, builders should use an \"entropy matrix\" based on two axes of uncertainty:",[36,37,38,46],"ol",{},[39,40,41,45],"li",{},[42,43,44],"strong",{},"Step Uncertainty:"," How predictable is the path to completing the task?",[39,47,48,51],{},[42,49,50],{},"Criteria Uncertainty:"," How clear is the rubric for verifying that the task was done correctly?",[53,54,55,61,67],"ul",{},[39,56,57,60],{},[42,58,59],{},"Low Step Uncertainty:"," If the steps are highly predictable, do not use an agent. Write a script instead.",[39,62,63,66],{},[42,64,65],{},"High Step Uncertainty:"," If the task is too unpredictable, it likely falls out of the model's distribution and will result in sparse rewards.",[39,68,69,72],{},[42,70,71],{},"High Criteria Uncertainty:"," If verifying the work is as difficult as doing the work, the agent provides no net efficiency gain.",[17,74,76],{"id":75},"the-sweet-spot-verification-first-design","The Sweet Spot: Verification-First Design",[22,78,79],{},"The ideal tasks for agents are those that resemble NP-style problems: they are computationally expensive to solve but relatively easy to verify. When a task has a repeatable verification pattern, the most effective strategy is to build a secondary agent specifically tasked with verifying the output of the primary agent. This approach allows for \"measurement at the speed of compute,\" effectively creating a modern equivalent of James Watt’s \"horsepower\"—a legible metric that helps users justify the ROI of adopting agentic workflows.",{"title":81,"searchDepth":82,"depth":82,"links":83},"",2,[84,85,86],{"id":19,"depth":82,"text":20},{"id":30,"depth":82,"text":31},{"id":75,"depth":82,"text":76},[88],"Product Strategy",null,"md",false,{"content_references":93,"triage":102},[94,99],{"type":95,"title":96,"author":97,"context":98},"other","Ramp blog post on overspending and underusing","Ramp","cited",{"type":95,"title":100,"author":101,"context":98},"Post on solving code review","Noah Hine",{"relevance":103,"novelty":104,"quality":104,"actionability":104,"composite":105,"reasoning":106},5,4,4.35,"Category: AI & LLMs. The article addresses the measurement problem in AI agents, providing a framework (entropy matrix) that builders can use to evaluate tasks for AI integration, which directly addresses the audience's need for practical applications. It offers actionable insights on task selection and verification, making it relevant and useful for product builders.",true,"\u002Fsummaries\u002F1c6f383e47085f06-measuring-ai-agents-the-entropy-matrix-and-mousepo-summary","2026-09-10 15:00:39","2026-09-11 03:11:48",{"title":5,"description":81},{"loc":108},"1c6f383e47085f06","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=8KkibGU_DDY","summaries\u002F1c6f383e47085f06-measuring-ai-agents-the-entropy-matrix-and-mousepo-summary",[119,120,121,122],"ai-tools","agents","product-strategy","roi","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F8KkibGU_DDY\u002Fhqdefault.jpg","Agents suffer from a measurement problem where token spend is often decoupled from actual value. To succeed, builders must move beyond token-counting to an 'entropy matrix' approach, identifying tasks where verification is cheaper than execution.","This talk argues that AI agents currently suffer from a \"measurement problem\" where token consumption is mistaken for value. The speaker proposes moving away from tracking raw compute costs toward an entropy-based framework that maps task uncertainty against verification difficulty to determine when an agent is actually worth the 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Risks of AI Press Tours: Lessons from Tilly Norwood's Malfunctions",{"provider":7,"model":8,"input_tokens":7839,"output_tokens":7840,"processing_time_ms":7841,"cost_usd":7842},4154,434,2809,0.0016895,{"type":14,"value":7844,"toc":7873},[7845,7849,7852,7856,7859],[17,7846,7848],{"id":7847},"the-failure-of-real-time-ai-interaction","The Failure of Real-Time AI Interaction",[22,7850,7851],{},"The promotional tour for Tilly Norwood, an AI-generated actress created by Particle6 Group, serves as a cautionary tale for deploying AI in unscripted, high-stakes environments. By attempting to conduct seventy-five simultaneous interviews, the model exposed significant reliability issues, most notably a public 'malfunction' where the agent abruptly switched to speaking Chinese during a live interview with Piers Morgan and Tom Conti. This incident underscores the fragility of current conversational AI when pushed beyond controlled, scripted environments.",[17,7853,7855],{"id":7854},"strategic-missteps-in-ai-deployment","Strategic Missteps in AI Deployment",[22,7857,7858],{},"Beyond the technical failures, the incident raises questions about the strategic intent behind such deployments. The author suggests two primary possibilities:",[36,7860,7861,7867],{},[39,7862,7863,7866],{},[42,7864,7865],{},"Technical Immaturity:"," The model lacks the robust guardrails and latency management required for real-time, multi-party dialogue, leading to 'crossed wires' that manifest as incoherent or off-topic responses.",[39,7868,7869,7872],{},[42,7870,7871],{},"Calculated Controversy:"," Particle6 Group may be leveraging the 'uncanny valley' and the viral nature of public failure to generate buzz. By making the AI intentionally or predictably flawed, the company ensures media coverage that a standard, high-quality AI performance might not have garnered. However, this strategy risks trivializing the product and alienating audiences who expect a baseline of competence from AI-driven entertainment.",{"title":81,"searchDepth":82,"depth":82,"links":7874},[7875,7876],{"id":7847,"depth":82,"text":7848},{"id":7854,"depth":82,"text":7855},[131],{"content_references":7879,"triage":7884},[7880],{"type":95,"title":7881,"author":7882,"context":7883},"Misaligned","Particle6 Group","mentioned",{"relevance":7885,"novelty":7885,"quality":104,"actionability":82,"composite":7886,"reasoning":7887},3,3.05,"Category: AI & LLMs. The article discusses the deployment of AI agents in high-stakes environments, which is relevant to the audience's interest in AI engineering and product strategy. While it provides insights into the failures of the AI actress, it lacks concrete actionable steps for product builders to apply in their own projects.","\u002Fsummaries\u002F4215ed0c60a66649-the-risks-of-ai-press-tours-lessons-from-tilly-nor-summary","2026-09-19 00:12:07","2026-09-19 03:10:37",{"title":7837,"description":81},{"loc":7888},"4215ed0c60a66649","TechCrunch — AI","article","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F09\u002F18\u002Ftilly-norwoods-press-tour-is-going-about-as-well-as-youd-expect-for-an-ai\u002F","summaries\u002F4215ed0c60a66649-the-risks-of-ai-press-tours-lessons-from-tilly-nor-summary",[119,120,121],"The disastrous press tour of AI 'actress' Tilly Norwood highlights the technical and strategic failures of deploying unrefined AI agents in high-stakes, real-time public interactions.",[],"8LV_EDXH0MJdCzlCmlA5FjrHvhNw3rUpPkpr4lv1upE",{"id":7903,"title":7904,"ai":7905,"body":7910,"categories":7964,"created_at":89,"date_modified":89,"description":81,"extension":90,"faq":89,"featured":91,"kicker_label":89,"meta":7965,"navigation":107,"path":7975,"published_at":7976,"question":89,"scraped_at":7977,"seo":7978,"sitemap":7979,"source_id":7980,"source_name":7894,"source_type":7895,"source_url":7981,"stem":7982,"tags":7983,"thumbnail_url":89,"tldr":7984,"tweet":89,"unknown_tags":7985,"__hash__":7986},"summaries\u002Fsummaries\u002F6e2c10698576e8b7-anthropic-unifies-claude-interface-for-seamless-ai-summary.md","Anthropic Unifies Claude Interface for Seamless AI Workflows",{"provider":7,"model":8,"input_tokens":7906,"output_tokens":7907,"processing_time_ms":7908,"cost_usd":7909},3958,497,3272,0.001735,{"type":14,"value":7911,"toc":7959},[7912,7916,7919,7923,7926,7952,7956],[17,7913,7915],{"id":7914},"eliminating-friction-through-unified-routing","Eliminating Friction Through Unified Routing",[22,7917,7918],{},"Anthropic is addressing user confusion by consolidating its previously fragmented interface into a single, cohesive workspace. Previously, users had to manually navigate between separate tabs for standard chat, Cowork (agentic tasks), and Artifacts. The new design removes this cognitive overhead by allowing Claude to automatically route requests to the appropriate engine based on the user's intent. This shift reflects a broader trend in AI product design: moving away from feature-siloed interfaces toward a single, agentic surface that adapts to the task at hand.",[17,7920,7922],{"id":7921},"expanding-capabilities-for-document-and-presentation-workflows","Expanding Capabilities for Document and Presentation Workflows",[22,7924,7925],{},"Beyond interface consolidation, Anthropic is introducing native, structured document and presentation tools. These features allow users to:",[53,7927,7928,7934,7940,7946],{},[39,7929,7930,7933],{},[42,7931,7932],{},"Create and Edit:"," Generate slides and documents directly within the Claude interface.",[39,7935,7936,7939],{},[42,7937,7938],{},"Export:"," Convert AI-generated presentations into PDF or PowerPoint formats.",[39,7941,7942,7945],{},[42,7943,7944],{},"Collaborate:"," Use commenting and section-based editing, similar to modern cloud-based productivity suites.",[39,7947,7948,7951],{},[42,7949,7950],{},"Cross-Platform Continuity:"," Start a task on desktop and track progress or perform edits via the mobile app, supported by an upgraded memory layer that maintains context across sessions.",[17,7953,7955],{"id":7954},"strategic-rollout","Strategic Rollout",[22,7957,7958],{},"The update is currently being deployed to Pro and Max tier subscribers across web, desktop, and mobile platforms, with plans to expand to free and team tiers in the future. By integrating these productivity tools directly into the chat interface, Anthropic is positioning Claude as a comprehensive workspace rather than just a conversational chatbot, directly competing with the integrated ecosystems of other major AI providers.",{"title":81,"searchDepth":82,"depth":82,"links":7960},[7961,7962,7963],{"id":7914,"depth":82,"text":7915},{"id":7921,"depth":82,"text":7922},{"id":7954,"depth":82,"text":7955},[131],{"content_references":7966,"triage":7972},[7967,7970],{"type":7968,"title":7969,"context":7883},"tool","Claude",{"type":7968,"title":7971,"context":7883},"ChatGPT",{"relevance":103,"novelty":104,"quality":104,"actionability":7885,"composite":7973,"reasoning":7974},4.15,"Category: AI & LLMs. The article discusses the unification of AI tools into a single interface, addressing a specific pain point of user confusion and enhancing productivity workflows, which is highly relevant for product builders. It provides insights into new features that can be directly applied to improve user experience in AI-powered products.","\u002Fsummaries\u002F6e2c10698576e8b7-anthropic-unifies-claude-interface-for-seamless-ai-summary","2026-09-16 16:30:00","2026-09-17 03:12:19",{"title":7904,"description":81},{"loc":7975},"6e2c10698576e8b7","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F09\u002F16\u002Fanthropic-merges-claude-chat-and-cowork-in-one-interface\u002F","summaries\u002F6e2c10698576e8b7-anthropic-unifies-claude-interface-for-seamless-ai-summary",[119,120,121],"Anthropic has merged its chat and Cowork interfaces into a single, unified workspace, allowing Claude to automatically route tasks between chat, interactive artifacts, and new document\u002Fpresentation tools without manual tab switching.",[],"0tFMvW43TtM0gjHYf5Im6n28jmycyAE0ElAgEOOr60s",{"id":7988,"title":7989,"ai":7990,"body":7995,"categories":8049,"created_at":89,"date_modified":89,"description":81,"extension":90,"faq":89,"featured":91,"kicker_label":89,"meta":8050,"navigation":107,"path":8062,"published_at":8063,"question":89,"scraped_at":8064,"seo":8065,"sitemap":8066,"source_id":8067,"source_name":7894,"source_type":7895,"source_url":8068,"stem":8069,"tags":8070,"thumbnail_url":89,"tldr":8072,"tweet":89,"unknown_tags":8073,"__hash__":8074},"summaries\u002Fsummaries\u002Fa6e0c2c45467c587-building-agent-native-communication-platforms-summary.md","Building Agent-Native Communication Platforms",{"provider":7,"model":8,"input_tokens":7991,"output_tokens":7992,"processing_time_ms":7993,"cost_usd":7994},4441,738,3783,0.00221725,{"type":14,"value":7996,"toc":8044},[7997,8001,8004,8007,8011,8014,8034,8038,8041],[17,7998,8000],{"id":7999},"the-problem-with-meat-proxies-in-modern-workflows","The Problem with 'Meat Proxies' in Modern Workflows",[22,8002,8003],{},"Traditional messaging platforms like Slack and Microsoft Teams were designed for human-to-human communication. When companies integrate AI agents into these environments, they often force a human to act as a \"meat proxy\"—manually relaying information, context, and outputs between the AI and the rest of the team. This creates a significant bottleneck, as the human becomes a messenger rather than a strategic decision-maker.",[22,8005,8006],{},"Sara Du, founder of Ando, argues that agents should not be treated as mere apps to be installed, but as active participants in shared conversations. By forcing agents to exist outside the core communication loop, legacy platforms fail to capture the full potential of AI-driven coordination.",[17,8008,8010],{"id":8009},"agent-native-architecture","Agent-Native Architecture",[22,8012,8013],{},"Ando is built from the ground up to treat AI agents as first-class citizens. Key architectural differences include:",[53,8015,8016,8022,8028],{},[39,8017,8018,8021],{},[42,8019,8020],{},"Agent Identity and Autonomy:"," Agents possess their own inboxes and identities, allowing them to participate in channels, DMs, and group conversations as naturally as human employees.",[39,8023,8024,8027],{},[42,8025,8026],{},"Contextual Awareness:"," Agents can monitor live calls (via transcription) and browse channels to understand the \"why\" behind team decisions. They can join conversations proactively without needing to be tagged.",[39,8029,8030,8033],{},[42,8031,8032],{},"Proactive Coordination:"," Rather than waiting for human approval, agents can identify cross-channel redundancies. For example, if an agent detects two separate channels discussing the same issue, it can merge the conversations, provide context, and suggest a resolution without human intervention.",[17,8035,8037],{"id":8036},"the-strategic-shift-from-execution-to-judgment","The Strategic Shift: From Execution to Judgment",[22,8039,8040],{},"Du posits that the goal of agent-native communication is to allow small teams to operate at the scale of much larger organizations. By offloading execution, research, and coordination to agents, human roles shift toward higher-level functions: judgment, strategy, and decision-making.",[22,8042,8043],{},"While Ando faces stiff competition from incumbents like Slack and Microsoft, as well as niche competitors like Buzz, the company is betting that building an agent-native foundation provides a structural advantage over legacy platforms attempting to retrofit AI into existing architectures. The platform is currently serving teams in software, real estate, and finance, with a focus on proving that agents can manage coordination tasks more efficiently than humans due to their ability to process vast amounts of information in real-time.",{"title":81,"searchDepth":82,"depth":82,"links":8045},[8046,8047,8048],{"id":7999,"depth":82,"text":8000},{"id":8009,"depth":82,"text":8010},{"id":8036,"depth":82,"text":8037},[88],{"content_references":8051,"triage":8060},[8052,8054,8056,8058],{"type":7968,"title":8053,"context":7883},"Ando",{"type":7968,"title":8055,"context":7883},"Slack",{"type":7968,"title":8057,"context":7883},"Microsoft Teams",{"type":7968,"title":8059,"context":7883},"Buzz",{"relevance":103,"novelty":104,"quality":104,"actionability":104,"composite":105,"reasoning":8061},"Category: AI & LLMs. The article discusses a novel approach to integrating AI agents into communication platforms, addressing a specific pain point of traditional messaging systems that rely on human intermediaries. It provides actionable insights into how agent-native architecture can enhance team workflows, making it relevant for product builders looking to implement AI in their tools.","\u002Fsummaries\u002Fa6e0c2c45467c587-building-agent-native-communication-platforms-summary","2026-09-24 14:31:00","2026-09-25 03:18:26",{"title":7989,"description":81},{"loc":8062},"a6e0c2c45467c587","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F09\u002F24\u002Fando-eyes-slack-as-it-builds-team-messaging-platform-for-humans-and-agents-to-work-together\u002F","summaries\u002Fa6e0c2c45467c587-building-agent-native-communication-platforms-summary",[120,8071,119,121],"saas","Ando is a team messaging platform that treats AI agents as first-class participants rather than external integrations, aiming to eliminate the 'meat proxy' bottleneck where humans manually relay information between agents and teams.",[],"mXRzZBiR9eB717UhYIUUS7ODgjgxpS2dD9wE6_Du1vU",{"id":8076,"title":8077,"ai":8078,"body":8083,"categories":8174,"created_at":89,"date_modified":89,"description":81,"extension":90,"faq":89,"featured":91,"kicker_label":89,"meta":8175,"navigation":107,"path":8187,"published_at":8188,"question":89,"scraped_at":8189,"seo":8190,"sitemap":8191,"source_id":8192,"source_name":8193,"source_type":115,"source_url":8194,"stem":8195,"tags":8196,"thumbnail_url":8197,"tldr":8198,"tweet":8199,"unknown_tags":8200,"__hash__":8201},"summaries\u002Fsummaries\u002F7b04701805b8dd29-moving-beyond-token-consumption-to-outcome-based-a-summary.md","Moving Beyond Token Consumption to Outcome-Based AI",{"provider":7,"model":8,"input_tokens":8079,"output_tokens":8080,"processing_time_ms":8081,"cost_usd":8082},5081,695,3409,0.00231275,{"type":14,"value":8084,"toc":8169},[8085,8089,8092,8106,8110,8113,8139,8142,8146,8149],[17,8086,8088],{"id":8087},"the-failure-of-proxy-metrics","The Failure of Proxy Metrics",[22,8090,8091],{},"For many organizations, the initial phase of AI adoption relied on activity-based metrics—specifically token consumption—as a proxy for engagement and value. This led to two opposing but equally flawed strategies:",[53,8093,8094,8100],{},[39,8095,8096,8099],{},[42,8097,8098],{},"Tokenmaxxing:"," The belief that higher usage equals higher value. This often results in bloated workflows where AI is used indiscriminately, masking a lack of actual business impact.",[39,8101,8102,8105],{},[42,8103,8104],{},"Token Minimization:"," A reactive cost-cutting measure that restricts context windows, prompts, and model access. This often backfires; by stripping away critical architectural context or domain constraints, teams save on input tokens but incur significantly higher costs in debugging, rework, and system instability.",[17,8107,8109],{"id":8108},"shifting-to-valuemaxxing","Shifting to Valuemaxxing",[22,8111,8112],{},"\"Valuemaxxing\" shifts the focus from consumption volume to measurable operational outcomes. Success should be measured by metrics that reflect business value, such as:",[53,8114,8115,8121,8127,8133],{},[39,8116,8117,8120],{},[42,8118,8119],{},"Deployment velocity:"," Number of successful deployments completed.",[39,8122,8123,8126],{},[42,8124,8125],{},"Efficiency gains:"," Developer time saved on specific tasks.",[39,8128,8129,8132],{},[42,8130,8131],{},"Quality improvements:"," Reduction in vulnerabilities or technical debt.",[39,8134,8135,8138],{},[42,8136,8137],{},"Rework avoidance:"," Decreased time spent fixing AI-generated errors.",[22,8140,8141],{},"In this framework, higher token consumption is justified if it directly correlates with these outcomes. The goal is not to use less AI, but to use it more effectively by treating AI resources with the same rigor as cloud infrastructure or database performance.",[17,8143,8145],{"id":8144},"orchestration-over-selection","Orchestration Over Selection",[22,8147,8148],{},"As AI models become commoditized infrastructure, the primary differentiator is no longer the model itself, but the system built around it. Effective AI strategy now requires:",[53,8150,8151,8157,8163],{},[39,8152,8153,8156],{},[42,8154,8155],{},"Model Orchestration:"," Moving away from single-model reliance. IDC predicts that by 2028, 70% of large-scale AI deployments will utilize multiple models to balance performance and cost.",[39,8158,8159,8162],{},[42,8160,8161],{},"Systemic Context Management:"," Prioritizing high-quality context hygiene, planning before execution, and robust workflow orchestration.",[39,8164,8165,8168],{},[42,8166,8167],{},"Platform Accountability:"," Platform leaders must provide visibility into the link between AI consumption and operational outcomes. Platforms should offer administrative controls, governance, and analytics that allow teams to track the ROI of their AI workflows rather than just the bill.",{"title":81,"searchDepth":82,"depth":82,"links":8170},[8171,8172,8173],{"id":8087,"depth":82,"text":8088},{"id":8108,"depth":82,"text":8109},{"id":8144,"depth":82,"text":8145},[88],{"content_references":8176,"triage":8185},[8177,8181],{"type":95,"title":8178,"author":8179,"context":8180},"Valuemaxxing","Mark Boroditsky","recommended",{"type":8182,"title":8183,"publisher":8184,"context":98},"report","IDC AI Predictions 2028","IDC",{"relevance":103,"novelty":104,"quality":104,"actionability":104,"composite":105,"reasoning":8186},"Category: Product Strategy. The article provides a fresh perspective on measuring AI success by focusing on operational outcomes rather than token consumption, addressing a key pain point for product-minded builders. It offers actionable metrics like deployment velocity and efficiency gains that can be directly applied to improve AI strategies.","\u002Fsummaries\u002F7b04701805b8dd29-moving-beyond-token-consumption-to-outcome-based-a-summary","2026-09-21 11:00:44","2026-09-22 03:27:09",{"title":8077,"description":81},{"loc":8187},"7b04701805b8dd29","IBM Technology","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=FheRqx7DjbU","summaries\u002F7b04701805b8dd29-moving-beyond-token-consumption-to-outcome-based-a-summary",[119,121,8071,120],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FFheRqx7DjbU\u002Fhqdefault.jpg","Measuring AI success by token consumption leads to either wasteful 'tokenmaxxing' or counterproductive 'token minimization.' Organizations should instead adopt 'valuemaxxing'—a strategy that prioritizes measurable operational outcomes like deployment speed and rework reduction over raw usage volume.","This video argues that organizations should stop using token consumption as a primary metric for AI success, as it encourages either wasteful \"tokenmaxxing\" or counterproductive cost-cutting. It advocates for [Valuemaxxing](https:\u002F\u002Fibm.biz\u002F~XUdepadwq), a shift toward measuring operational outcomes like deployment speed and rework reduction.",[],"amC2U7Z1KDucm1lOBWc65eb51dVK9whJezqBc7az8uc"]