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The Daylight Principle in Agentic AI
The Daylight Principle in Agentic AI. What Is It and Why is it Critical? AI’s Context Challenge. In the previous post in our Augmented, Not Artificial series, we argued that the shift from rigid SaaS to adaptive, intelligent platforms represents the most consequential evolution in enterprise software since the cloud. But recognizing that shift is the easy part. Realizing its benefits is harder.
In our last post, we discussed how data is the new general-purpose technology and that is why at super{set} we form data-driven companies from scratch. But new technologies are a promise, not a sudden phase change.
The wheel. Electricity. The automobile. These are technologies that had a disproportionate impact on the merits of their first practical use-case; but beyond that, because they enabled so much in terms of subsequent innovation, economic historians call them “general-purpose technologies” or GPTs...
Coming up with new company ideas is easy: we take the day off, go to the park, and let the thoughts arrive like butterflies. Maybe we grab a coconut from that guy for a little buzz. While this describes a pleasant day in San Francisco, it couldn’t be further from the truth of what we do at super{set}. If only we could pull great ideas out of thin air. Unfortunately, it just doesn’t work that way.
This post was written by MarkovML Co-Founder, Lindsey Meyl, as part of our #PassTheMic series.
The super{set} studio model for early-stage venture It is still early days for the startup studio model. We know this because at super{set} we still get questions from experienced operators and investors. One investor that we’ve known for years recently asked us: “you have a fund — aren’t you just a venture capital firm with a different label?”
This post was written by Ketch Sales Director, Sheridan Rice, as part of our #PassTheMic series.
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