Nathaniel Whittemore
speaker
48,511 appearances
192 recordings
3 series
first heard Oct 2025
last heard 5d ago
Nathaniel Whittemore’s voice in public audio — every appearance, attributed to the second.
Trend
recordings per month · last 12 monthsRecordings per month over the last 12 months — 192 in all, peaking in Jun 2026 with 31.
Appearances
AI native company feature 15 is the Citizen Developers STLC.
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It's an approach that Alex has talked about elsewhere as well that enables non-technical employees to take a solution that they are building with coding tools from idea to production with the company's governance, access, versioning, and software conventions built into it.
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It's basically a process of reconciling the things that the non-engineers are making with the way that engineers build.
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Again, not with the idea of replacing software engineering in any way, shape, or form, but in order to have the new things that people are building for themselves or their teams, or even some segment of customers based on the part of the customer lifecycle that they touch, to have that all contiguous with the engineering organization.
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It reflects again that shifting relationship between different parts of the organization in this new AI native space.
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Feature 16 gets to the sort of loop engineering that we covered in the webinar that I've shared on the show earlier this week.
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Make non-engineering workflow self-improving by learning from previous runs through external performance metrics and internal evaluations.
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The idea of loops is that instead of prompting agents, we give them a goal and bumpers around what they can do, and design a process that they can loop through over and over again until they achieve that goal.
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One of the necessary requirements of a loop is some verifiable success metric that is objective rather than subjective.
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I.e., I
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I need to achieve an X percentage result on this test is a lot more definable and outcome goal than is our interface needs to look good.
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AI native organizations are going to be good at creating those sort of clear metrics of success, not just for the easy deterministic tasks, but for the broader array of knowledge work tasks that don't necessarily have that sort of success criteria built in natively.
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Feature 17 is really two parts.
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One is to use some AI ROI framework to have an idea of what the organization is looking for out of its AI efforts and to be able to measure against that.
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The second part is a little bit more opinionated from Alex about the way to set that up, with his recommendation being experimental scaling and optimization phases and bets placed across infrastructure, innovation, and efficiency.
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Whatever the phases that you end up using and the way that you organize different types of efforts, I think that the big recommendation here is to have a complex ROI architecture that can understand the goal of different efforts as being different from one another, but the organization having the ability
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ability to judge them even if they are different all within the same framework.
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Feature 18 is my Doctor Strange theory of agentic work come to life.
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The idea is that AI native organizations will use agent swarms to deploy many, many, many, perhaps hundreds, perhaps thousands of paid marketing creative variations for testing before increasing spend on ads.
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One of the things that I underestimated when I was first thinking about that, which by the way, I still think is completely inevitable, was the way in which compute constraints in the short term would limit the viability.
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Showing 2061–2080 of 48,511 · page 104 of 2426
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