@levie: One of the key architectural questions of the 21st century in business will be how you maximize your corporate IP in th…

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Summary

The article argues that as AI models commoditize intelligence, businesses must focus on uniquely leveraging their corporate IP through workflows, evals, and routing to create value, presenting opportunities in the applied AI layer.

One of the key architectural questions of the 21st century in business will be how you maximize your corporate IP in the form of decisions, insights, workflow patterns, and best practices in a world where so much intelligence is packed into AI models. One might think these questions could just get bitter lessoned out of existence, but in reality they become even more germane as intelligence becomes more powerful. In a world where any firm also has access to frontier intelligence, understanding how you leverage it uniquely becomes a critical question. That’s why so much value is left to be created between the enterprise and the underlying AI itself. Having evals for your workflows, ensuring that you can route models from different tiers of intelligence, capturing traces in a way that improve your own workflows, and making sure the value of your information compounds as AI gets better all become critical considerations. Which is also why there’s so much opportunity right now in the applied AI layer. The companies that help figure this out for other enterprises will be in the best position to win the next enterprise workloads.
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Cached at: 07/12/26, 09:02 PM

One of the key architectural questions of the 21st century in business will be how you maximize your corporate IP in the form of decisions, insights, workflow patterns, and best practices in a world where so much intelligence is packed into AI models.

One might think these questions could just get bitter lessoned out of existence, but in reality they become even more germane as intelligence becomes more powerful. In a world where any firm also has access to frontier intelligence, understanding how you leverage it uniquely becomes a critical question.

That’s why so much value is left to be created between the enterprise and the underlying AI itself. Having evals for your workflows, ensuring that you can route models from different tiers of intelligence, capturing traces in a way that improve your own workflows, and making sure the value of your information compounds as AI gets better all become critical considerations.

Which is also why there’s so much opportunity right now in the applied AI layer. The companies that help figure this out for other enterprises will be in the best position to win the next enterprise workloads.

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TLDR AI

This article argues that AI intelligence is becoming commoditized, similar to compute and storage, and that the most valuable companies will not be model builders but those who own customer relationships, proprietary data, and workflows.