This One Chart Exposes Why Most Companies Are Failing At AI
Episode
17 min
Read time
2 min
Topics
Productivity, Design & UX, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓The Red-Blue Gap: Anthropic's chart reveals that observed AI deployment (red) is a fraction of theoretical coverage (blue) across every industry, including coding, finance, and legal. Only 8.6% of companies have deployed an AI agent in production, representing the real opportunity.
- ✓Electricity Factory Analogy: When factories first adopted electricity in the 1880s, less than 5% of mechanical power came from electric motors by 1900 because companies kept old layouts. AI productivity gains only arrive when companies redesign workflows entirely around AI, not swap old processes for new tools.
- ✓RAPID-5 Framework: A five-stage AI transformation model — Reveal (map workflows), Architect (design AI-native operating model), Proof (two-week real-world sprints), InGrain (identity shift via peer learning), Dynamize (90-day reassessment cycles) — provides a structured path from current state to AI-native operations.
- ✓Forward-Deployed AI Skill: To close the deployment gap internally, record team workflows via Loom, extract transcripts, and feed them into an AI tool with a structured transformation prompt. This replicates what forward-deployed engineers at OpenAI and Anthropic do for enterprise clients.
What It Covers
Kieran and Kipp argue that AI model capabilities are no longer the competitive differentiator — using Anthropic's viral chart showing a massive gap between theoretical and actual AI deployment across industries to make their case.
Key Questions Answered
- •The Red-Blue Gap: Anthropic's chart reveals that observed AI deployment (red) is a fraction of theoretical coverage (blue) across every industry, including coding, finance, and legal. Only 8.6% of companies have deployed an AI agent in production, representing the real opportunity.
- •Electricity Factory Analogy: When factories first adopted electricity in the 1880s, less than 5% of mechanical power came from electric motors by 1900 because companies kept old layouts. AI productivity gains only arrive when companies redesign workflows entirely around AI, not swap old processes for new tools.
- •RAPID-5 Framework: A five-stage AI transformation model — Reveal (map workflows), Architect (design AI-native operating model), Proof (two-week real-world sprints), InGrain (identity shift via peer learning), Dynamize (90-day reassessment cycles) — provides a structured path from current state to AI-native operations.
- •Forward-Deployed AI Skill: To close the deployment gap internally, record team workflows via Loom, extract transcripts, and feed them into an AI tool with a structured transformation prompt. This replicates what forward-deployed engineers at OpenAI and Anthropic do for enterprise clients.
Notable Moment
Despite 84% of general consumers never having used AI and a massive theoretical automation opportunity across industries, the top 5% of enterprise AI users are already orders of magnitude ahead of everyone else in deployment intensity.
Episode Transcript
On today's show, we're gonna show you the single most important chart in AI that explains why your company isn't going to be a winner just because you use the best models. You heard that right. The best AI models are not gonna dictate who wins and who loses in AI. We're gonna give you the actual winning formula and how you can implement it at your company today. All of that and more on this episode of Marketing Against a Grain. Here's a quick word from HubSpot. HubSpot helped Tumblr solve a big problem. They needed to move fast to produce trending content, but their marketing team was stuck waiting on engineers to code every single email campaign. Now they use HubSpot's customer platform to email real time trending content to millions of users in just seconds. The impact? Three times more engagement, double the content creation. Wanna move faster like Tumblr? Visit hubspot.com. Alright, Kieth. We are here with yet another AI model, GPT 5.4 is that another model, beats lots of benchmarks, apparently one of the best models on the planet. And we're kinda here to argue in this twelve to fifteen minute video that it doesn't even matter. It does. That this model does not even matter, doesn't matter how good it is. Model capabilities are not the important thing right now in the AI industry. Yeah. We're gonna instead tell you what the most important thing. Instead of the models getting better, what that is, and how you can leverage that for your business to actually change growth in this new era. And, Kieran, based on that, there's a chart that's going viral from Anthropic that I think was the lightning rod moment for the conversation we wanted to have. And a lot of people are interpreting it one way. We have, like, a very different interpretation of it. So GBT 5.4, great code and model, great intelligence model, beat and entropic and lots of benchmarks, but we've been here before. Every single model that comes out kind of goes a little bit more up the benchmarks. Now there are ways that they kind of align themselves to benchmark to do really well. But we are believers that model capabilities are already very, very good and actually giving people even more capable models is not gonna make much of a difference right now because of this chart. So this chart is being shared pretty widely around x, and it's from Entropic. So for a picture Entropic, they're putting out these to try to show what the impact of AI could be across industries. So what it's showing you is theoretical AI coverage, which means how much of that industry could be theoretically automated with AI. And there are no surprises here. AI is really good at coding and math. It's very good at finance. It's very good at engineering type roles. It's very good at legal. It's very good at, like, arts and media, pretty …
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