Nathaniel Whittemore
speaker
51,978 appearances
203 recordings
3 series
first heard Oct 2025
last heard 2d 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 — 203 in all, peaking in Jun 2026 with 31.
Appearances
I can see this being very useful for fraud and risk, support routing, moderation, PR and QA automation, lead scoring, compliance, workflow orchestration, and agent routing.
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Early tech, obviously, he says, but the category itself makes a lot of sense.
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Now, interestingly, Matt Stockton points out that in some ways, companies adopting this amount to a post-LLM AI technology, making pre-LLM machine learning techniques a little bit more accessible.
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As he writes,
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Lots and lots of problems in business are classification or regression problems.
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Lots and lots of companies don't know that the types of problems they have are solvable by classic machine learning methods.
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They often solve them with people in process instead of technology.
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With the emergence and popularity of LLMs, more companies are thinking, maybe we can use AI for that, and are solving classification and regression problems with LLMs.
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This is good in some ways because companies are potentially automating some manual work, but also bad in some ways because it's often the wrong tool for the job, and possibly not as good as classic ML methods for what they are trying to do.
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But he points out the classical techniques require you to label your data, train a model, and host that model somewhere.
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They aren't as easy to use compared to calling an LLM API, and it requires you and your org to be aware of those techniques and capable of investing in them.
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Without going too far on the analogy, he basically says one way to look at JEV is as a UX for using LLM-style user interaction patterns for classical ML techniques.
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Now, one interesting question that comes up is whether this is for individuals or teams building systems.
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And the short answer is that while it is absolutely both, it also puts a fine point on the multiplayer AI themes that we've been talking about recently.
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Certainly individuals could use this sort of capability to have personal tools that sort an inbox against their own priorities, check drafts of their writing against an editorial rubric, rank saved articles against research interests.
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or flag commitments in meeting transcripts, things like that that are going to personally help you do your work better.
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But I think that where this sort of technique is going to really shine is in the domain of teamwork that happens through small judgments about who needs to know, who should act, and whose approval is required.
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When an agent is serving a single person, it gets pretty far simply by learning that person's preferences.
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An agent operating across a team, however, needs to understand the relationships between people
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Work.
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Showing 2741–2760 of 51,978 · page 138 of 2599
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