{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/69ab3b7c7036d739021982df/6af7e6f9cc76a302c4ecb879?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"AI Evals for Buying AI: Keep More of the Savings","description":"<p>For deeper playbooks and analysis: <a href=\"https://natesnewsletter.substack.com/\" rel=\"noopener noreferrer\" target=\"_blank\">https://natesnewsletter.substack.com/</a></p><p><br></p><p>What&#39;s really happening when AI keeps improving but customers have less reason to pay for the upgrade?</p><p>The common story is that more capable models automatically create more revenue — but the reality is that buyers can test what is good enough and move routine work to cheaper models.</p><p><br></p><p>In this video, I share the inside scoop on who gets paid as AI becomes more useful and the market for intelligence becomes more demanding.</p><ul><li>Why token use can rise while the bill stays the same.</li><li>How evals turn model selection into a purchasing decision.</li><li>What Ramp&#39;s spending data reveals about adoption and pricing power.</li><li>Where productivity gains go when customers expect lower prices.</li><li>What stays valuable as models improve and technical work becomes easier.</li></ul><p>For business leaders, the decision is about the full cost of useful work: model spend, review, repairs, and the surrounding systems. Set a baseline on real assignments, then ask what value your business can retain as customers and competitors adapt.</p><p><br></p><p>Subscribe for daily AI strategy and news.</p><p>Hosted on Acast. See acast.com/privacy for more information.</p>","author_name":"Nate B. Jones"}